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train · 44.6k rows

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0917f06f-41d0-4af5-ba5a-477ec7c850a9	discriminative-online-learning-for-fast-video	1904.08630	null	http://arxiv.org/abs/1904.08630v1	http://arxiv.org/pdf/1904.08630v1.pdf	Discriminative Online Learning for Fast Video Object Segmentation	We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effectively handle appearance changes and similar background objects, a robust representation of the target is required. Previous approaches either rely on fine-tuning a segmentation network on the first frame, or employ generative appearance models. Although partially successful, these methods often suffer from impractically low frame rates or unsatisfactory robustness. We propose a novel approach, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions. Importantly, we design a specialized loss and customized optimization techniques to enable highly efficient online training. Our light-weight target model is integrated into a carefully designed segmentation network, trained offline to enhance the predictions generated by the target model. Extensive experiments are performed on three datasets. Our approach achieves an overall score of over 70 on YouTube-VOS, while operating at 25 frames per second.	['Michael Felsberg', 'Fahad Shahbaz Khan', 'Felix Järemo Lawin', 'Martin Danelljan', 'Andreas Robinson']	2019-04-18	null	null	null	null	['one-shot-visual-object-segmentation']	['computer-vision']	[ 3.78304303e-01 -9.90287438e-02 -2.37787887e-01 -4.28455174e-01 -7.72348702e-01 -5.25936186e-01 2.55414546e-01 -2.29221731e-01 -3.61431897e-01 3.00407410e-01 -3.61064494e-01 -1.51605889e-01 5.22472739e-01 -3.93667430e-01 -7.30998814e-01 -6.22375429e-01 1.47353441e-01 3.11608851e-01 7.77894020e-01 1.29365325e-01 1.13186307e-01 3.71703565e-01 -1.42537618e+00 1.48513347e-01 9.23685849e-01 1.44880009e+00 3.51569891e-01 8.37718308e-01 -8.68487954e-02 8.94532621e-01 -4.96708333e-01 -3.96610916e-01 6.37591839e-01 -5.74946642e-01 -6.86896503e-01 7.25874305e-01 7.01812267e-01 -4.60878015e-01 -3.68771255e-01 1.06675911e+00 2.48121068e-01 1.59015790e-01 4.55608785e-01 -1.12982321e+00 -1.41600430e-01 1.76969945e-01 -6.68302536e-01 4.05197531e-01 1.28374279e-01 3.83882374e-01 9.15007174e-01 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b7836506-c809-43a9-9578-deb01294ca8d	unsupervised-fine-tuning-for-text-clustering	null	null	https://aclanthology.org/2020.coling-main.482	https://aclanthology.org/2020.coling-main.482.pdf	Unsupervised Fine-tuning for Text Clustering	Fine-tuning with pre-trained language models (e.g. BERT) has achieved great success in many language understanding tasks in supervised settings (e.g. text classification). However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering. In this paper, we propose a novel method to fine-tune pre-trained models unsupervisedly for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss. Experiments on three text clustering datasets (namely TREC-6, Yelp, and DBpedia) show that our model outperforms the baseline methods and achieves state-of-the-art results.	['Ming Zhou', 'Xingxing Zhang', 'Lei Cui', 'Furu Wei', 'Shaohan Huang']	2020-12-01	null	null	null	coling-2020-8	['text-clustering']	['natural-language-processing']	[-2.46123403e-01 -1.21007599e-01 -2.96029687e-01 -8.65129113e-01 -8.20765555e-01 -4.47213352e-01 6.77078247e-01 5.94480038e-01 -6.97754562e-01 5.09210646e-01 3.28381300e-01 -2.82040477e-01 -8.74872878e-02 -5.60995817e-01 -5.98943949e-01 -6.35811508e-01 1.27596259e-01 1.14699554e+00 3.87916341e-02 -6.48107678e-02 3.45245481e-01 -1.80734456e-01 -1.33305478e+00 7.83427060e-01 9.21615362e-01 3.47905576e-01 7.14875907e-02 5.28299093e-01 -4.95033324e-01 7.70921111e-01 -4.76576805e-01 -6.33538842e-01 -2.26795137e-01 -2.79152066e-01 -1.19541240e+00 2.47556597e-01 1.31774560e-01 2.20347077e-01 -2.21530467e-01 8.00430715e-01 3.50488126e-01 4.69962120e-01 8.93937290e-01 -8.83331358e-01 -7.46596932e-01 1.40629494e+00 -7.49640644e-01 -8.41216594e-02 -1.35744244e-01 -5.27200043e-01 1.19414711e+00 -7.46926486e-01 3.07830423e-01 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9a466d6c-5b3b-4531-a294-9687d9f0b787	ecpe-2d-emotion-cause-pair-extraction-based	null	null	https://aclanthology.org/2020.acl-main.288	https://aclanthology.org/2020.acl-main.288.pdf	ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction	In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis. It aims at extracting the potential pairs of emotions and their corresponding causes in a document. To solve this task, the existing research employed a two-step framework, which first extracts individual emotion set and cause set, and then pair the corresponding emotions and causes. However, such a pipeline of two steps contains some inherent flaws: 1) the modeling does not aim at extracting the final emotion-cause pair directly; 2) the errors from the first step will affect the performance of the second step. To address these shortcomings, in this paper we propose a new end-to-end approach, called ECPE-Two-Dimensional (ECPE-2D), to represent the emotion-cause pairs by a 2D representation scheme. A 2D transformer module and two variants, window-constrained and cross-road 2D transformers, are further proposed to model the interactions of different emotion-cause pairs. The 2D representation, interaction, and prediction are integrated into a joint framework. In addition to the advantages of joint modeling, the experimental results on the benchmark emotion cause corpus show that our approach improves the F1 score of the state-of-the-art from 61.28{\%} to 68.89{\%}.	['Zixiang Ding', 'Rui Xia', 'Jianfei Yu']	2020-07-01	null	null	null	acl-2020-6	['emotion-cause-pair-extraction']	['natural-language-processing']	[ 5.29266819e-02 -1.09347388e-01 2.15963751e-01 -5.03020346e-01 -7.86747098e-01 -3.58612210e-01 5.49401224e-01 1.11566089e-01 -1.04098115e-02 4.90568876e-01 3.60258520e-01 1.66133523e-01 -3.12780052e-01 -4.91411835e-01 -1.03053048e-01 -5.61544538e-01 -1.16027184e-01 7.47600198e-02 -1.25108078e-01 -2.85319239e-01 2.47811392e-01 1.01599731e-01 -1.73827076e+00 4.95065570e-01 9.44414735e-01 1.21332240e+00 -4.21231017e-02 4.14355546e-01 -6.67718470e-01 7.71723807e-01 -5.23944855e-01 -4.31672484e-01 -1.42316446e-01 -5.48046112e-01 -6.02453232e-01 -1.52218089e-01 -4.06980813e-01 -7.59590194e-02 1.85550243e-01 7.86897123e-01 6.10548735e-01 1.15950860e-01 8.05777133e-01 -1.50147641e+00 -2.33112112e-01 4.67808455e-01 -8.39965701e-01 -2.81214356e-01 6.62835658e-01 -4.90799397e-01 9.86175120e-01 -1.15453339e+00 2.84870684e-01 1.41498864e+00 5.85322142e-01 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bb66435f-8440-492b-b997-73087f5e0c4b	wire-wavelet-implicit-neural-representations	2301.05187	null	https://arxiv.org/abs/2301.05187v1	https://arxiv.org/pdf/2301.05187v1.pdf	WIRE: Wavelet Implicit Neural Representations	Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function employed in its multilayer perceptron (MLP) network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Wavelet Implicit neural REpresentation (WIRE) uses a continuous complex Gabor wavelet activation function that is well-known to be optimally concentrated in space-frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness.	['Richard G. Baraniuk', 'Ashok Veeraraghavan', 'Guha Balakrishnan', 'Jasper Tan', 'Daniel LeJeune', 'Vishwanath Saragadam']	2023-01-05	null	http://openaccess.thecvf.com//content/CVPR2023/html/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.pdf	cvpr-2023-1	['image-inpainting']	['computer-vision']	[ 6.26560926e-01 -2.15237498e-01 1.21315040e-01 -2.53785968e-01 -5.25202751e-01 -4.30604890e-02 5.49305856e-01 -2.37668082e-01 -2.89365232e-01 6.48503959e-01 1.45364061e-01 6.60188943e-02 -5.03910482e-01 -8.35453510e-01 -6.11528814e-01 -1.00322855e+00 1.70212373e-01 -1.25370517e-01 4.69520576e-02 -3.16905558e-01 3.63211751e-01 8.55285585e-01 -1.57488406e+00 2.09727198e-01 6.13359869e-01 1.21674478e+00 -2.02352721e-02 5.80344975e-01 2.11510569e-01 9.92693722e-01 -6.70848846e-01 -8.92066061e-02 2.00506747e-01 -2.89672166e-01 -4.40952867e-01 -1.21191405e-01 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d9583ca8-a250-453b-959d-07dd93914a32	undiff-unsupervised-voice-restoration-with	2306.00721	null	https://arxiv.org/abs/2306.00721v1	https://arxiv.org/pdf/2306.00721v1.pdf	UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model	This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional manner, it can be adapted to different tasks including degradation inversion, neural vocoding, and source separation. In this paper, we, first, tackle the challenging problem of unconditional waveform generation by comparing different neural architectures and preconditioning domains. After that, we demonstrate how the trained unconditional diffusion could be adapted to different tasks of speech processing by the means of recent developments in post-training conditioning of diffusion models. Finally, we demonstrate the performance of the proposed technique on the tasks of bandwidth extension, declipping, vocoding, and speech source separation and compare it to the baselines. The codes will be released soon.	['Dmitry Vetrov', 'Nicholas Babaev', 'Ivan Shchekotov', 'Pavel Andreev', 'Anastasiia Iashchenko']	2023-06-01	null	null	null	null	['bandwidth-extension', 'bandwidth-extension']	['audio', 'speech']	[ 4.18404877e-01 2.12954521e-01 2.28190020e-01 -9.78860855e-02 -1.02270865e+00 -4.76942658e-01 6.01164401e-01 -3.30128759e-01 -2.26198003e-01 5.91475308e-01 6.79687798e-01 -5.31999290e-01 -6.68542162e-02 -3.34185243e-01 -6.45081162e-01 -9.15046811e-01 1.09379150e-01 2.09337831e-01 1.59966037e-01 -1.05434492e-01 3.30357719e-03 4.82499689e-01 -1.26706171e+00 2.35955194e-01 8.50042224e-01 7.97415495e-01 3.93115133e-01 9.74943042e-01 3.01993132e-01 5.99652410e-01 -7.01864660e-01 -2.12099150e-01 -1.45078182e-01 -6.06633127e-01 -6.26111686e-01 -1.38945431e-01 1.33064792e-01 -2.38690451e-01 -5.51621199e-01 9.77313101e-01 9.95200336e-01 4.60498422e-01 1.06167960e+00 -7.82239854e-01 -1.00746536e+00 8.30414176e-01 -1.75460771e-01 3.29339743e-01 3.01564604e-01 -1.11742511e-01 7.54636765e-01 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7d854708-a6d5-4ab8-93f5-d0e33f434353	transfer-knowledge-from-natural-language-to	2301.09017	null	https://arxiv.org/abs/2301.09017v2	https://arxiv.org/pdf/2301.09017v2.pdf	Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?	Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in various downstream applications. However, whether the learned knowledge by LLMs can be transferred to clinical cardiology remains unknown. In this work, we aim to bridge this gap by transferring the knowledge of LLMs to clinical Electrocardiography (ECG). We propose an approach for cardiovascular disease diagnosis and automatic ECG diagnosis report generation. We also introduce an additional loss function by Optimal Transport (OT) to align the distribution between ECG and language embedding. The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection. Our approach is able to generate high-quality cardiac diagnosis reports and also achieves competitive zero-shot classification performance even compared with supervised baselines, which proves the feasibility of transferring knowledge from LLMs to the cardiac domain.	['Ding Zhao', 'Douglas Weber', 'Emerson Liu', 'Michael Rosenberg', 'Mengdi Xu', 'Jiacheng Zhu', 'William Han', 'JieLin Qiu']	2023-01-21	null	null	null	null	['electrocardiography-ecg']	['methodology']	[ 3.51972938e-01 2.93531805e-01 -1.28250554e-01 -3.06827515e-01 -1.37110555e+00 -4.24141437e-01 3.83354157e-01 5.33058226e-01 -2.37864882e-01 7.41714954e-01 4.41348940e-01 -2.97237307e-01 -6.42113984e-02 -7.10951388e-01 -2.94295341e-01 -5.37932813e-01 -2.19492882e-01 5.36342144e-01 -2.69705534e-01 1.88075438e-01 -8.90032053e-02 1.92448601e-01 -8.29297900e-01 3.92003536e-01 7.79466450e-01 7.70953357e-01 -7.19354451e-02 1.03130507e+00 1.02711715e-01 9.01100814e-01 -5.87356985e-01 -5.49725890e-01 -1.03189237e-01 -9.52346861e-01 -8.51637602e-01 6.10914491e-02 1.13653585e-01 -2.20414162e-01 -2.01598108e-01 6.02701724e-01 1.24872255e+00 -2.86040127e-01 8.42040598e-01 -1.03029466e+00 -1.03226984e+00 5.63218296e-01 -2.52724230e-01 4.95575041e-01 1.27078384e-01 1.39558345e-01 9.73904014e-01 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5e769b5d-c4eb-4f3c-9998-1305ddddf64a	contrastive-self-supervised-learning-of	null	null	https://openreview.net/forum?id=Py4VjN6V2JX	https://openreview.net/pdf?id=Py4VjN6V2JX	Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations	Contrastive self-supervised learning has delivered impressive results in many audio-visual recognition tasks. However, existing approaches optimize for learning either global representations useful for high-level understanding tasks such as classification, or local representations useful for tasks such as audio-visual source localization and separation. While they produce satisfactory results in their intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for. In this work, we propose a versatile self-supervised approach to learn audio-visual representations that can generalize to both the tasks which require global semantic information (e.g. classification) and the tasks that require fine-grained spatio-temporal information (e.g. localization). We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given the corresponding audio information. To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classification, lip reading, deepfake detection, and sound source localization.	['Yale Song', 'Daniel McDuff', 'Zhaoyang Zeng', 'Shuang Ma']	2021-01-01	null	null	null	null	['sound-classification']	['audio']	[ 4.50624168e-01 -3.24609488e-01 -3.93042237e-01 -3.83247703e-01 -1.37316072e+00 -5.71438611e-01 5.34197450e-01 2.85690278e-01 -3.88890728e-02 4.62370068e-01 5.63959122e-01 1.85499936e-02 -1.03129521e-01 -2.21783891e-01 -7.59145081e-01 -7.57584035e-01 -2.43504331e-01 2.12917384e-03 1.15423776e-01 3.72218043e-02 2.74415582e-01 4.28091377e-01 -2.17514515e+00 6.23728633e-01 4.05430019e-01 1.18720913e+00 1.75829545e-01 9.51721549e-01 -2.43701434e-04 8.73728037e-01 -5.23582041e-01 4.50148880e-01 -1.03723228e-01 -6.24327302e-01 -9.17839646e-01 1.25092626e-01 8.72039020e-01 -8.16289261e-02 -1.42333999e-01 8.53323638e-01 5.73788941e-01 4.15023834e-01 8.59711349e-01 -1.58440900e+00 -5.40735841e-01 3.14344168e-01 -4.90389943e-01 4.31426197e-01 5.08087397e-01 1.07835233e-01 1.31098914e+00 -8.00602496e-01 1.08809762e-01 1.32936239e+00 5.32356620e-01 5.32584548e-01 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b9c4548c-da68-427e-af55-88307fe24969	neural-pose-transfer-by-spatially-adaptive	2003.07254	null	https://arxiv.org/abs/2003.07254v2	https://arxiv.org/pdf/2003.07254v2.pdf	Neural Pose Transfer by Spatially Adaptive Instance Normalization	Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional studies assume that the paired source and target meshes are existed with the point-wise correspondences of user annotated landmarks/mesh points, which requires heavy labelling efforts. On the other hand, the generalization ability of deep models is limited, when the source and target meshes have different identities. To break this limitation, we proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component -- spatially adaptive instance normalization. Our model does not require any correspondences between the source and target meshes. Extensive experiments show that the proposed model can effectively transfer deformation from source to target meshes, and has good generalization ability to deal with unseen identities or poses of meshes. Code is available at https://github.com/jiashunwang/Neural-Pose-Transfer .	['yinda zhang', 'xiangyang xue', 'Yanwei Fu', 'Chao Wen', 'Tianyun Zou', 'Jiashun Wang', 'Haitao Lin']	2020-03-16	neural-pose-transfer-by-spatially-adaptive-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.pdf	cvpr-2020-6	['pose-transfer']	['computer-vision']	[ 1.29587024e-01 1.46289796e-01 5.13625517e-02 -3.74494106e-01 -5.09478927e-01 -5.14262378e-01 3.16763848e-01 -2.16411054e-01 -2.30642468e-01 6.13058031e-01 -1.49458930e-01 3.69075239e-01 1.83592930e-01 -9.37012970e-01 -1.00219357e+00 -5.48842967e-01 3.79624993e-01 5.55852294e-01 3.04499984e-01 -3.39349836e-01 2.16327384e-02 4.24724907e-01 -1.28388715e+00 -1.15744688e-01 8.52336943e-01 9.64166224e-01 1.35830462e-01 1.69819742e-01 4.10014987e-02 -1.19647831e-01 -4.54343140e-01 -4.81262326e-01 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501a5ec6-4e11-4c6b-9eb8-d6b2368999d7	multilingual-contextual-adapters-to-improve	2307.00759	null	https://arxiv.org/abs/2307.00759v1	https://arxiv.org/pdf/2307.00759v1.pdf	Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages	Connectionist Temporal Classification (CTC) models are popular for their balance between speed and performance for Automatic Speech Recognition (ASR). However, these CTC models still struggle in other areas, such as personalization towards custom words. A recent approach explores Contextual Adapters, wherein an attention-based biasing model for CTC is used to improve the recognition of custom entities. While this approach works well with enough data, we showcase that it isn't an effective strategy for low-resource languages. In this work, we propose a supervision loss for smoother training of the Contextual Adapters. Further, we explore a multilingual strategy to improve performance with limited training data. Our method achieves 48% F1 improvement in retrieving unseen custom entities for a low-resource language. Interestingly, as a by-product of training the Contextual Adapters, we see a 5-11% Word Error Rate (WER) reduction in the performance of the base CTC model as well.	['Sravan Bodapati', 'Brady Houston', 'Saket Dingliwal', 'Devang Kulshreshtha']	2023-07-03	null	null	null	null	['speech-recognition', 'automatic-speech-recognition']	['speech', 'speech']	[ 1.58365309e-01 1.47943392e-01 -1.36741951e-01 -3.30392927e-01 -1.22141647e+00 -4.64439988e-01 7.20197499e-01 1.73256025e-01 -8.34970176e-01 5.25035441e-01 5.07332385e-01 -5.09935439e-01 2.49696240e-01 -1.99648544e-01 -5.36050856e-01 -5.79296172e-01 2.51981527e-01 3.92209679e-01 2.90629447e-01 -4.24863368e-01 5.20953152e-04 3.88228834e-01 -1.13870907e+00 5.25468588e-01 7.35447049e-01 6.25289738e-01 3.28825951e-01 6.63355589e-01 9.43477324e-04 4.27010417e-01 -7.73919225e-01 -6.25621617e-01 -1.00849047e-01 -3.24552916e-02 -9.85250473e-01 -1.93664879e-01 1.64472565e-01 9.34477001e-02 -4.77352083e-01 6.17678702e-01 6.78799331e-01 3.92122716e-01 4.94264811e-01 -7.09187269e-01 -4.92745787e-01 9.26433086e-01 -1.62089318e-01 4.35537994e-01 3.34351927e-01 -1.17888721e-02 1.23437917e+00 -1.11366498e+00 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4.22310144e-01 -2.50803113e-01 -5.13554513e-01 -1.01278806e+00 -1.93685949e-01 2.86742538e-01 2.71488398e-01 -3.30557525e-01 -4.59698886e-01 -9.80149508e-02]	[14.281710624694824, 6.7858567237854]
9499f29e-984d-4530-9bed-2858510bff88	neural-network-models-for-stock-selection	1906.05327	null	https://arxiv.org/abs/1906.05327v1	https://arxiv.org/pdf/1906.05327v1.pdf	Neural Network Models for Stock Selection Based on Fundamental Analysis	Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS.	['Luiz Fernando Capretz', 'Danny Ho', 'Yuxuan Huang']	2019-06-12	null	null	null	null	['stock-prediction']	['time-series']	[-6.00596011e-01 -2.33453795e-01 -7.46823922e-02 -3.42788935e-01 3.46018225e-01 -4.83553678e-01 6.17304921e-01 -1.70594007e-01 -4.21383023e-01 8.18698049e-01 5.51540330e-02 -4.63807464e-01 -8.54250908e-01 -1.07224369e+00 6.01858869e-02 -3.84255648e-01 -2.33454749e-01 4.06049848e-01 2.60844260e-01 -8.03365886e-01 1.05698287e+00 7.29863346e-01 -1.51776588e+00 2.29276955e-01 5.43741345e-01 1.61360955e+00 -1.90591827e-01 3.34036946e-01 -1.52421296e-01 1.31607056e+00 -9.84016716e-01 -5.84457695e-01 7.46922910e-01 -1.82204992e-01 -1.99978769e-01 -6.81579590e-01 7.11149424e-02 -5.48444808e-01 -2.77047575e-01 1.20513499e+00 1.53010890e-01 3.81580621e-01 6.86467648e-01 -1.06539226e+00 -1.00455284e+00 7.98503339e-01 8.01531747e-02 9.08432126e-01 -4.53605503e-02 -2.62123585e-01 7.48741686e-01 -1.03572857e+00 1.49599716e-01 8.24975550e-01 7.56671071e-01 3.75030860e-02 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0bcc26e4-e597-463b-a4c9-8067113e1d0c	next-sentence-prediction-helps-implicit	null	null	https://aclanthology.org/D19-1586	https://aclanthology.org/D19-1586.pdf	Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains	Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other. Current discourse relation classifiers fall short in this respect. We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a next-sentence prediction task, and thus encode a representation of likely next sentences. The BERT-based model outperforms the current state of the art in 11-way classification by 8{\%} points on the standard PDTB dataset. Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms the state of the art system around 15{\%} points.	['Wei Shi', 'Vera Demberg']	2019-11-01	null	null	null	ijcnlp-2019-11	['implicit-discourse-relation-classification']	['natural-language-processing']	[ 6.30634725e-01 1.02681565e+00 -4.64474738e-01 -3.11406970e-01 -8.84615064e-01 -4.71452177e-01 1.06217253e+00 5.16694546e-01 -3.23603243e-01 1.08369172e+00 6.54948711e-01 -7.14871407e-01 -1.35900483e-01 -8.72117937e-01 -7.77787447e-01 -2.89321333e-01 9.20835696e-03 6.78518713e-01 5.80243230e-01 -5.95092714e-01 1.66832224e-01 3.06561086e-02 -1.43063366e+00 8.71234536e-01 5.59507370e-01 8.20872545e-01 2.24433765e-01 7.58736551e-01 -2.18529209e-01 1.71640897e+00 -8.03677678e-01 -6.19288146e-01 -5.07088244e-01 -5.34929931e-01 -1.59411204e+00 -3.10997725e-01 5.61665557e-02 -3.23598444e-01 -1.99037418e-01 6.56418025e-01 1.55866608e-01 -2.25460250e-02 7.62255430e-01 -7.98311830e-01 -2.88869113e-01 1.17756593e+00 -2.19670326e-01 4.67448205e-01 5.76390624e-01 -4.81962830e-01 1.36850345e+00 -5.38500428e-01 1.08181691e+00 1.29151952e+00 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dfc704a2-1054-42b9-b572-a4c1cb1f5717	figo-enhanced-fingerprint-identification	2208.05615	null	https://arxiv.org/abs/2208.05615v2	https://arxiv.org/pdf/2208.05615v2.pdf	FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques	Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint identification of fingerprints is still in its earliest stage. The performance of traditional \textit{Automatic Fingerprint Identification System} (AFIS) depends on the presence of valid minutiae points and still requires human expert assistance in feature extraction and identification stages. Based on this motivation, we propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques (FIGO). Our solution contains two components: fingerprint enhancement tier and fingerprint identification tier. First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier. With the proposed enhancement algorithm, the fingerprint identification model's performance is significantly improved. Furthermore, we develop another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint device's recognition accuracy. Experimental results show that our proposed Pix2pix model has better support than the baseline approach for fingerprint identification. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process. Two twin convolutional neural networks (CNNs) with shared weights and parameters are used to obtain the feature vectors in this process. Using the proposed method, we demonstrate that it is possible to learn necessary information from only one training sample with high accuracy.	['Mahmoud Abouyoussef', 'Ibrahim Yilmaz']	2022-08-11	null	null	null	null	['one-shot-learning']	['methodology']	[ 7.71307230e-01 -2.37269133e-01 -1.57182425e-01 -4.94702011e-01 -2.68834323e-01 -6.12043321e-01 3.74209851e-01 -2.58761585e-01 -4.21351314e-01 5.35980940e-01 -5.52567482e-01 -2.46027872e-01 -3.69552851e-01 -1.13288856e+00 -8.54813814e-01 -5.79238236e-01 -2.44273953e-02 1.04233541e-01 7.40925819e-02 -6.64205253e-02 6.17176652e-01 9.46327150e-01 -1.48212850e+00 1.86994910e-01 8.81312728e-01 1.12090242e+00 -1.08520640e-02 8.09301019e-01 -2.38555461e-01 5.21363616e-01 -4.27626014e-01 -7.11978912e-01 5.83086967e-01 8.77321512e-03 -5.96691966e-01 -4.62162644e-01 6.98802650e-01 -6.71353936e-01 -2.49190733e-01 1.07064128e+00 6.06996000e-01 -2.28051528e-01 5.40027678e-01 -1.05003583e+00 -5.88444054e-01 3.77840817e-01 -5.08185267e-01 -2.07347557e-01 1.29709512e-01 -7.53509700e-02 3.72188658e-01 -7.85227537e-01 5.49247503e-01 9.42437649e-01 1.20066941e+00 4.46563512e-01 -1.31769097e+00 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744e2a12-71f5-4b02-a44e-d5f2d8c2e4f9	neglectable-effect-of-brain-mri-data	2204.05278	null	https://arxiv.org/abs/2204.05278v3	https://arxiv.org/pdf/2204.05278v3.pdf	Negligible effect of brain MRI data preprocessing for tumor segmentation	Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such as anatomy alignment, voxel resampling, signal intensity equalization, image denoising, and localization of regions of interest. Although a preprocessing pipeline standardizes image appearance, its influence on the quality of image segmentation and on other downstream tasks in deep neural networks has never been rigorously studied. We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in intra- and inter-dataset training scenarios. Our results demonstrate that most popular standardization steps add no value to the network performance; moreover, preprocessing can hamper model performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization. Finally, we show that the contribution of skull-stripping in data preprocessing is almost negligible if measured in terms of estimated tumor volume. We show that the only essential transformation for accurate deep learning analysis is the unification of voxel spacing across the dataset. In contrast, inter-subjects anatomy alignment in the form of non-rigid atlas registration is not necessary and intensity equalization steps (denoising, bias-field correction and histogram matching) do not improve models' performance. The study code is accessible online \footnote{https://github.com/MedImAIR/brain-mri-processing-pipeline}.	['Mikhail Belyaev', 'Boris Shirokikh', 'Andrey Golanov', 'Svetlana Zolotova', 'Anvar Kurmukov', 'Alexandra Dalechina', 'Polina Druzhinina', 'Ekaterina Kondrateva']	2022-04-11	null	null	null	null	['skull-stripping']	['medical']	[ 3.34929675e-01 -1.03530481e-01 2.36593578e-02 -6.68475449e-01 -6.85154259e-01 -5.55747449e-01 4.42769289e-01 7.18150914e-01 -1.02032840e+00 5.23112297e-01 8.42334926e-02 -3.95670831e-01 -2.49546066e-01 -5.77534020e-01 -8.99160504e-01 -6.04096889e-01 6.44618794e-02 4.24081296e-01 2.56615072e-01 -4.34142631e-03 1.67627677e-01 7.36906588e-01 -9.34259117e-01 -4.47543822e-02 8.85297954e-01 7.17077851e-01 1.50975883e-01 2.29576960e-01 -1.57994941e-01 3.58489156e-01 -4.94898081e-01 -1.74135551e-01 2.84325361e-01 -4.61692333e-01 -7.02440679e-01 -1.28899649e-01 3.75549674e-01 -3.47120911e-01 -1.83574334e-01 1.16928792e+00 8.52335393e-01 1.17537133e-01 5.54997206e-01 -7.68017590e-01 -2.16536447e-01 7.57198393e-01 -6.71226680e-01 6.35854900e-01 -5.08592188e-01 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343c3d39-1696-4a9e-bccb-9de977472dcf	extensive-deep-temporal-point-process	2110.09823	null	https://arxiv.org/abs/2110.09823v4	https://arxiv.org/pdf/2110.09823v4.pdf	An Empirical Study: Extensive Deep Temporal Point Process	Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks to the strong expressivity of deep neural networks, they are emerging as a promising choice for capturing the patterns in asynchronous sequences, in the context of temporal point process. In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization. We introduce most of recently proposed models by dismantling them into the four parts, and conduct experiments by remodularizing the first three parts with the same learning strategy for a fair empirical evaluation. Besides, we extend the history encoders and conditional intensity function family, and propose a Granger causality discovery framework for exploiting the relations among multi-types of events. Because the Granger causality can be represented by the Granger causality graph, discrete graph structure learning in the framework of Variational Inference is employed to reveal latent structures of the graph. Further experiments show that the proposed framework with latent graph discovery can both capture the relations and achieve an improved fitting and predicting performance.	['Stan. Z. Li', 'Zhangyang Gao', 'Lirong Wu', 'Cheng Tan', 'Haitao Lin']	2021-10-19	null	null	null	null	['graph-structure-learning']	['graphs']	[ 6.88856319e-02 5.64984195e-02 -3.40737015e-01 -8.79051760e-02 -3.13216180e-01 -5.69158316e-01 8.90798926e-01 2.99544483e-01 1.20778641e-04 5.45393407e-01 4.83375996e-01 -2.84616858e-01 -5.33745944e-01 -9.34323370e-01 -7.64833450e-01 -6.66001022e-01 -7.60488093e-01 5.40725946e-01 1.47650629e-01 1.43195108e-01 -1.02528289e-01 3.00736904e-01 -1.10100472e+00 8.22416916e-02 5.09178638e-01 8.61714125e-01 -3.61746997e-02 6.57343268e-01 -2.25791603e-01 1.47876513e+00 -4.27950472e-01 -5.39197862e-01 -3.54004316e-02 -5.65293849e-01 -4.98859733e-01 -5.53738922e-02 -4.73234475e-01 -4.04042244e-01 -1.01965809e+00 8.61584067e-01 1.56206116e-01 9.32842866e-02 7.73729205e-01 -1.47309685e+00 -5.67000329e-01 1.03604400e+00 -5.16986251e-01 6.31345928e-01 3.75952661e-01 -2.91707236e-02 1.46174765e+00 -5.01654327e-01 5.06686270e-01 1.27739966e+00 5.93908250e-01 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c293918e-64d9-4631-9734-38a20e10b857	radam-texture-recognition-through-randomized-1	2303.04554	null	https://arxiv.org/abs/2303.04554v1	https://arxiv.org/pdf/2303.04554v1.pdf	RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps	Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named \textbf{R}andom encoding of \textbf{A}ggregated \textbf{D}eep \textbf{A}ctivation \textbf{M}aps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pre-trained deep convolutional network using a Randomized Autoencoder (RAE). The RAE is trained locally to each image using a closed-form solution, and its decoder weights are used to compose a 1-dimensional texture representation that is fed into a linear SVM. This means that no fine-tuning or backpropagation is needed. We explore RADAM on several texture benchmarks and achieve state-of-the-art results with different computational budgets. Our results suggest that pre-trained backbones may not require additional fine-tuning for texture recognition if their learned representations are better encoded.	['Odemir M. Bruno', 'Bernard De Baets', 'Wesley N. Gonçalves', 'Lucas C. Ribas', 'Kallil M. Zielinski', 'Leonardo Scabini']	2023-03-08	radam-texture-recognition-through-randomized	https://arxiv.org/abs/2303.04554	https://arxiv.org/pdf/2303.04554.pdf	null	['texture-classification']	['computer-vision']	[ 5.18145978e-01 2.84056455e-01 -9.21589136e-02 -6.22936070e-01 -4.96037424e-01 -1.54192686e-01 5.10564685e-01 -1.04830489e-01 -3.81539643e-01 3.65689427e-01 -8.55630785e-02 -1.71224028e-01 5.19107794e-03 -1.09229970e+00 -1.20166981e+00 -1.15263331e+00 -3.76368165e-02 4.03333575e-01 3.88665348e-01 -3.04689944e-01 1.86783016e-01 6.48304284e-01 -1.72852600e+00 1.02351046e+00 4.58043218e-01 1.75936925e+00 2.67974108e-01 8.37089479e-01 -1.42320305e-01 8.68033111e-01 -4.01072860e-01 -4.18823123e-01 3.07018995e-01 -7.63202906e-02 -8.82908881e-01 1.82817221e-01 5.61779499e-01 -3.23343009e-01 -4.58054304e-01 8.00182223e-01 1.76752225e-01 8.96965060e-03 7.32256293e-01 -6.80962920e-01 -6.45477772e-01 4.69659090e-01 -4.55892414e-01 1.30713612e-01 -2.21411735e-01 -1.79410458e-01 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322aa261-4617-473d-bba6-4155ce69dac0	high-fidelity-audio-generation-and	2006.00877	null	https://arxiv.org/abs/2006.00877v2	https://arxiv.org/pdf/2006.00877v2.pdf	High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder	Unsupervised disentangled representation learning from the unlabelled audio data, and high fidelity audio generation have become two linchpins in the machine learning research fields. However, the representation learned from an unsupervised setting does not guarantee its' usability for any downstream task at hand, which can be a wastage of the resources, if the training was conducted for that particular posterior job. Also, during the representation learning, if the model is highly biased towards the downstream task, it losses its generalisation capability which directly benefits the downstream job but the ability to scale it to other related task is lost. Therefore, to fill this gap, we propose a new autoencoder based model named "Guided Adversarial Autoencoder (GAAE)", which can learn both post-task-specific representations and the general representation capturing the factors of variation in the training data leveraging a small percentage of labelled samples; thus, makes it suitable for future related tasks. Furthermore, our proposed model can generate audio with superior quality, which is indistinguishable from the real audio samples. Hence, with the extensive experimental results, we have demonstrated that by harnessing the power of the high-fidelity audio generation, the proposed GAAE model can learn powerful representation from unlabelled dataset leveraging a fewer percentage of labelled data as supervision/guidance.	['Björn W. Schuller', 'Rajib Rana', 'Kazi Nazmul Haque']	2020-06-01	null	null	null	null	['audio-generation']	['audio']	[ 4.79010612e-01 4.28059429e-01 4.28838134e-02 -4.50878590e-03 -7.72195280e-01 -3.60993773e-01 3.94937158e-01 -1.79378927e-01 -9.79218557e-02 7.72780895e-01 3.34708929e-01 3.56783234e-02 -2.30509505e-01 -8.61256301e-01 -6.96496427e-01 -1.10038197e+00 2.93323807e-02 8.70058015e-02 -1.67743832e-01 -4.58892941e-01 -2.24444389e-01 9.26224887e-02 -1.61816037e+00 1.16035290e-01 7.88362205e-01 9.90726829e-01 1.31334156e-01 4.71663207e-01 4.34814841e-01 7.65702426e-01 -7.09682226e-01 -2.98251301e-01 3.34508032e-01 -4.66938764e-01 -3.88869941e-01 -2.59744495e-01 -8.44314024e-02 -3.70060414e-01 -6.40253127e-01 8.55888367e-01 7.78047979e-01 1.91325590e-01 7.20461845e-01 -1.35846293e+00 -5.91418862e-01 8.69714439e-01 -3.05800259e-01 3.80314738e-02 1.19603470e-01 2.05480412e-01 9.78228688e-01 -5.51106930e-01 2.00687751e-01 1.02565730e+00 5.24723887e-01 6.53580844e-01 -1.15431190e+00 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5.15531600e-01 4.51134592e-01 -7.75338233e-01 -7.21086442e-01 -5.97601384e-02]	[15.213607788085938, 5.298123836517334]
c3ba396e-23b8-4b03-9d12-8df22807f937	selective-in-context-data-augmentation-for	2302.05096	null	https://arxiv.org/abs/2302.05096v1	https://arxiv.org/pdf/2302.05096v1.pdf	Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information	This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average).	['Dilek Hakkani-Tur', 'Yang Liu', 'Di Jin', 'Mahdi Namazifar', 'Devamanyu Hazarika', 'Sungjin Lee', 'Seokhwan Kim', 'Alexandros Papangelis', 'Yen-Ting Lin']	2023-02-10	null	null	null	null	['intent-detection']	['natural-language-processing']	[ 5.63248038e-01 2.68362015e-01 -2.76461363e-01 -3.64291906e-01 -1.14083493e+00 -4.07154202e-01 9.99918282e-01 2.81924814e-01 -6.58218443e-01 4.73252445e-01 6.63831174e-01 -1.53562576e-01 3.70525658e-01 -5.39236248e-01 -5.39767444e-01 -2.55547911e-01 -1.51419476e-01 3.47939074e-01 6.80668354e-02 -4.47813511e-01 2.68448889e-01 3.90123427e-02 -1.61593735e+00 4.93930995e-01 7.31813133e-01 6.68067276e-01 2.25977544e-02 8.47970128e-01 -6.53781816e-02 1.01608276e+00 -7.53769100e-01 -8.01095143e-02 2.37754107e-01 -4.68177199e-01 -4.62341428e-01 4.68151569e-02 4.22869802e-01 -4.88497227e-01 -2.95834810e-01 5.03275335e-01 3.47482473e-01 5.34476340e-01 7.17966318e-01 -1.19301248e+00 -6.19222105e-01 4.82514858e-01 -2.71510750e-01 3.72966468e-01 4.33321267e-01 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c2211214-667c-4a72-950f-ae353f5866e5	facebook-aaoaac3cfacebook-activity-event	null	null	https://aclanthology.org/O16-1022	https://aclanthology.org/O16-1022.pdf	Facebook 活動事件擷取系統(Facebook Activity Event Extraction System)[In Chinese]	null	['Chia-Hui Chang', 'Yuan-Hao Lin']	2016-10-01	facebook-facebook-activity-event-extraction	https://aclanthology.org/O16-1022	https://aclanthology.org/O16-1022.pdf	roclingijclclp-2016-10	['sequential-pattern-mining']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 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1a613d1d-92fa-47b7-bc06-eb7fa3816dd2	controlled-data-generation-via-insertion	null	null	https://aclanthology.org/2022.naacl-industry.7	https://aclanthology.org/2022.naacl-industry.7.pdf	Controlled Data Generation via Insertion Operations for NLU	Use of synthetic data is rapidly emerging as a realistic alternative to manually annotating live traffic for industry-scale model building. Manual data annotation is slow, expensive and not preferred for meeting customer privacy expectations. Further, commercial natural language applications are required to support continuously evolving features as well as newly added experiences. To address these requirements, we propose a targeted synthetic data generation technique by inserting tokens into a given semantic signature. The generated data are used as additional training samples in the tasks of intent classification and named entity recognition. We evaluate on a real-world voice assistant dataset, and using only 33% of the available training set, we achieve the same accuracy as training with all available data. Further, we analyze the effects of data generation across varied real-world applications and propose heuristics that improve the task performance further.	['Wael Hamza', 'Anna Rumshisky', 'Rahul Gupta', 'Haidar Khan', 'Yuval Merhav', 'Manoj Kumar']	null	null	null	null	naacl-acl-2022-7	['intent-classification']	['natural-language-processing']	[ 2.47137755e-01 2.61366785e-01 -3.38460118e-01 -8.61153245e-01 -8.74240041e-01 -7.55614460e-01 5.69642663e-01 1.49323270e-01 -5.74270129e-01 8.17014337e-01 2.15885743e-01 -4.19791281e-01 3.63456875e-01 -5.01740336e-01 -3.79061550e-01 6.17528409e-02 2.34781697e-01 8.44349205e-01 3.23265880e-01 -6.97161397e-03 7.21727088e-02 5.09586573e-01 -1.50649238e+00 5.44780970e-01 9.46458340e-01 1.02075553e+00 -1.38488576e-01 4.78170604e-01 -5.62361836e-01 7.11911619e-01 -8.71623039e-01 -6.16427779e-01 5.27302027e-01 -4.36234735e-02 -7.94600010e-01 3.94961596e-01 1.95933431e-01 -2.00806931e-01 5.28531410e-02 6.83284283e-01 5.87436974e-01 1.90513164e-01 1.99322999e-02 -1.76453388e+00 -2.26142928e-01 6.75936401e-01 -8.40732679e-02 -1.80964902e-01 4.84735787e-01 1.15046144e-01 9.82794285e-01 -7.57899642e-01 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3d748919-de9d-4813-a6af-ea4182347500	continuous-space-representations-of	null	null	https://aclanthology.info/papers/N15-1036/n15-1036	https://www.aclweb.org/anthology/N15-1036	Continuous Space Representations of Linguistic Typology and their Application to Phylogenetic Inference	null	['Yugo Murawaki']	2015-05-01	null	null	null	hlt-2015-5	['electrical-engineering']	['miscellaneous']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 -6.05450153e-01 -4.56729174e-01 1.43710867e-01 -1.88022718e-01 -5.35770595e-01 -3.36825520e-01 7.91536197e-02 -5.88986814e-01 -3.10633808e-01 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9b30478e-814a-46b9-bd81-7eb51c538d5c	neural-imaging-pipelines-the-scourge-or-hope	1902.10707	null	http://arxiv.org/abs/1902.10707v1	http://arxiv.org/pdf/1902.10707v1.pdf	Neural Imaging Pipelines - the Scourge or Hope of Forensics?	Forensic analysis of digital photographs relies on intrinsic statistical traces introduced at the time of their acquisition or subsequent editing. Such traces are often removed by post-processing (e.g., down-sampling and re-compression applied upon distribution in the Web) which inhibits reliable provenance analysis. Increasing adoption of computational methods within digital cameras further complicates the process and renders explicit mathematical modeling infeasible. While this trend challenges forensic analysis even in near-acquisition conditions, it also creates new opportunities. This paper explores end-to-end optimization of the entire image acquisition and distribution workflow to facilitate reliable forensic analysis at the end of the distribution channel, where state-of-the-art forensic techniques fail. We demonstrate that a neural network can be trained to replace the entire photo development pipeline, and jointly optimized for high-fidelity photo rendering and reliable provenance analysis. Such optimized neural imaging pipeline allowed us to increase image manipulation detection accuracy from approx. 45% to over 90%. The network learns to introduce carefully crafted artifacts, akin to digital watermarks, which facilitate subsequent manipulation detection. Analysis of performance trade-offs indicates that most of the gains can be obtained with only minor distortion. The findings encourage further research towards building more reliable imaging pipelines with explicit provenance-guaranteeing properties.	['Pawel Korus', 'Nasir Memon']	2019-02-27	null	null	null	null	['image-manipulation-detection']	['computer-vision']	[ 5.76762736e-01 -1.44050509e-01 4.75411974e-02 -1.81239426e-01 -9.92156506e-01 -8.73559415e-01 4.78803545e-01 3.94565076e-01 -5.54227352e-01 8.33716393e-02 -4.41186391e-02 -5.33008397e-01 1.05844826e-01 -5.01575530e-01 -9.57699537e-01 -2.73457617e-01 -2.08991468e-01 -8.93439204e-02 1.70885339e-01 4.40184951e-01 5.49725056e-01 6.60345972e-01 -1.51267552e+00 3.40732425e-01 4.93599981e-01 7.41364717e-01 -1.13167306e-02 8.83733690e-01 -2.29292959e-02 9.01653647e-01 -8.95338058e-01 -7.30479062e-01 6.04644597e-01 -1.08565442e-01 -4.64248776e-01 -1.18443519e-02 7.66298294e-01 -1.25161552e+00 -6.13098979e-01 8.77669692e-01 4.72817063e-01 -2.57979274e-01 2.35608056e-01 -1.33197784e+00 -8.89484704e-01 3.50417614e-01 -7.99259245e-01 3.46972197e-01 1.93554848e-01 8.37126970e-01 5.66508591e-01 -3.90143037e-01 7.99274385e-01 7.67990112e-01 9.62865055e-01 1.63766950e-01 -1.06616282e+00 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8792c599-1abf-42c9-95a9-46e6271bb7e6	mdaesf-cine-mri-reconstruction-based-on	2303.04968	null	https://arxiv.org/abs/2303.04968v2	https://arxiv.org/pdf/2303.04968v2.pdf	MDAMF: Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion	Cardiac cine magnetic resonance imaging not only requires higher imaging speed but also needs to address motion artifacts. Especially in the case of free-breathing, more motion artifacts are inevitably introduced. This poses higher demands on the reconstruction performance of the model and its ability to capture temporal information. Previous methods have not effectively utilized the temporal dimension information to compensate for motion artifacts. In order to fully leverage the spatiotemporal information and reduce the impact of motion artifacts, this paper proposes a motion-guided deformable alignment method with second-order bidirectional propagation. Furthermore, aligning adjacent frames may lead to low accuracy or misalignment issues, which are detrimental to subsequent fusion reconstruction. Previous methods have not sufficiently integrated and corrected the aligned feature information. This paper proposes a multi-resolution fusion method to further correct alignment errors or artifacts. Compared to other advanced methods, the proposed approach achieves better image reconstruction quality in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual effects. The source code will be made available on https://github.com/GtLinyer/MDAMF.	['Weikun Zhang', 'Keyan Chen', 'Qiaohong Liu', 'Yuanjie Lin', 'Yiman Liu', 'Xiaoxiang Han']	2023-03-09	null	null	null	null	['mri-reconstruction']	['computer-vision']	[ 2.92008042e-01 -3.30572635e-01 1.69559479e-01 -6.97865412e-02 -5.96272230e-01 -1.97564080e-01 1.27028838e-01 1.65442824e-02 -3.18391532e-01 6.44063652e-01 3.02410215e-01 9.70057677e-03 -2.63770878e-01 -3.41779858e-01 -1.27070382e-01 -8.19430053e-01 -1.74322844e-01 -3.12895358e-01 3.74308079e-01 1.49184406e-01 2.13011637e-01 2.29082793e-01 -1.00084388e+00 -2.03686710e-02 1.19199431e+00 7.38336325e-01 8.10184062e-01 4.62007403e-01 1.67135075e-01 7.64538050e-01 -1.68037415e-01 2.20097620e-02 2.19347566e-01 -6.89693153e-01 -6.67176127e-01 -1.20905852e-02 6.63862750e-02 -6.75321698e-01 -3.44576895e-01 1.13474023e+00 8.54140341e-01 1.55727014e-01 2.56310940e-01 -7.65879154e-01 -1.81522965e-01 2.83074707e-01 -8.54589105e-01 8.04460406e-01 1.64256722e-01 1.69940829e-01 1.74884170e-01 -8.24213207e-01 7.31797397e-01 6.52710795e-01 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-9.29132104e-01 -3.25546771e-01 4.31654066e-01 2.69492120e-01 -6.18603826e-01 -1.74694300e-01 1.21533565e-01]	[13.529254913330078, -2.448354959487915]
818d762a-9794-4be5-9e18-3329df969b2f	accelerating-the-evolutionary-algorithms-by	2210.06814	null	https://arxiv.org/abs/2210.06814v1	https://arxiv.org/pdf/2210.06814v1.pdf	Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function	In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimization. And simple but efficient $\epsilon$-greedy acquisition function is employed to find a promising solution in the surrogate model. Proximity Optimal Principle (POP) states that well-performed solutions have a similar structure, and there is a high probability of better solutions existing around the elite individual. Based on this hypothesis, in each generation of optimization, we replace the worst individual in Evolutionary Algorithms (EAs) with the elite individual to participate in the evolution process. To illustrate the scalability of our proposal, we combine our proposal with the Genetic Algorithm (GA), Differential Evolution (DE), and CMA-ES. Experimental results in CEC2013 benchmark functions show our proposal has a broad prospect to estimate the elite individual and accelerate the convergence of optimization.	['Masaharu Munetomo', 'Enzhi Zhang', 'Rui Zhong']	2022-10-13	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[-6.42549247e-02 -1.47791013e-01 3.32974255e-01 2.42696367e-02 -2.30248049e-01 -4.96162735e-02 2.28893682e-01 2.05885351e-01 -4.43914264e-01 1.14079642e+00 -2.30212793e-01 6.59187511e-02 -4.62520897e-01 -9.77896094e-01 -5.09191215e-01 -1.10169733e+00 1.27135605e-01 4.27704602e-01 -2.57177260e-02 -1.04415342e-01 7.07322121e-01 2.50178993e-01 -1.69587541e+00 -2.75665104e-01 1.61891794e+00 9.53111768e-01 4.53287274e-01 2.59368777e-01 -3.21540982e-01 -8.95142183e-02 -7.18818545e-01 -3.46563697e-01 3.82428207e-02 -7.03321874e-01 -4.95137364e-01 -2.71190941e-01 -7.79509544e-01 4.55200613e-01 2.54515171e-01 1.33271134e+00 7.38079786e-01 3.97898525e-01 7.98867583e-01 -9.06488836e-01 -5.76836288e-01 5.71034491e-01 -7.19718635e-01 -7.85551965e-02 2.50071496e-01 1.46089926e-01 5.68930507e-01 -9.09143031e-01 3.72031659e-01 1.10618603e+00 6.98658109e-01 3.24758828e-01 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efbdb5e5-347f-4c6a-9da8-43c1b7dd4da9	multijugate-dual-learning-for-low-resource	2305.16106	null	https://arxiv.org/abs/2305.16106v1	https://arxiv.org/pdf/2305.16106v1.pdf	Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System	Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency in low-resource scenarios can be enhanced by mining alignment information uncertain utterance and deterministic dialogue state. Therefore, we innovatively implement dual learning in task-oriented dialogues to exploit the correlation of heterogeneous data. In addition, the one-to-one duality is converted into a multijugate duality to reduce the influence of spurious correlations in dual training for generalization. Without introducing additional parameters, our method could be implemented in arbitrary networks. Extensive empirical analyses demonstrate that our proposed method improves the effectiveness of end-to-end task-oriented dialogue systems under multiple benchmarks and obtains state-of-the-art results in low-resource scenarios.	['Xipeng Qiu', 'Linyang Li', 'Yanjun Zheng', 'Xiaotian Zhang', 'ShiMin Li']	2023-05-25	null	null	null	null	['task-oriented-dialogue-systems']	['natural-language-processing']	[-1.41435519e-01 3.47394049e-01 -4.56084698e-01 -5.39772213e-01 -7.17377484e-01 -6.75610244e-01 7.22492337e-01 -2.98899323e-01 -4.39168274e-01 9.77470517e-01 4.36596215e-01 -4.57895011e-01 -8.62939805e-02 -5.10838866e-01 -1.60195500e-01 -3.99617285e-01 -1.13060281e-01 8.08859825e-01 3.69533189e-02 -8.70181978e-01 6.92620203e-02 -8.60532150e-02 -8.59692037e-01 5.77788770e-01 9.76861477e-01 6.41488791e-01 1.65569149e-02 6.48265839e-01 -2.44482264e-01 8.30447316e-01 -7.24750102e-01 -5.93533337e-01 5.53779781e-01 -4.41310853e-01 -1.08966255e+00 1.63097113e-01 -4.88998331e-02 -5.20026445e-01 -4.73537475e-01 7.25418389e-01 8.06440830e-01 2.92253345e-01 4.01266843e-01 -1.05865717e+00 -3.51667076e-01 7.56144166e-01 -5.84675014e-01 2.44321185e-03 3.60793829e-01 4.38042432e-01 1.17540193e+00 -9.08857107e-01 3.29524517e-01 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60dd1afb-eb1d-4d4b-8bc1-b23b7e183f10	domain-specific-author-attribution-based-on	1602.07393	null	http://arxiv.org/abs/1602.07393v1	http://arxiv.org/pdf/1602.07393v1.pdf	Domain Specific Author Attribution Based on Feedforward Neural Network Language Models	Authorship attribution refers to the task of automatically determining the author based on a given sample of text. It is a problem with a long history and has a wide range of application. Building author profiles using language models is one of the most successful methods to automate this task. New language modeling methods based on neural networks alleviate the curse of dimensionality and usually outperform conventional N-gram methods. However, there have not been much research applying them to authorship attribution. In this paper, we present a novel setup of a Neural Network Language Model (NNLM) and apply it to a database of text samples from different authors. We investigate how the NNLM performs on a task with moderate author set size and relatively limited training and test data, and how the topics of the text samples affect the accuracy. NNLM achieves nearly 2.5% reduction in perplexity, a measurement of fitness of a trained language model to the test data. Given 5 random test sentences, it also increases the author classification accuracy by 3.43% on average, compared with the N-gram methods using SRILM tools. An open source implementation of our methodology is freely available at https://github.com/zge/authorship-attribution/.	['Yufang Sun', 'Zhenhao Ge']	2016-02-24	null	null	null	null	['author-attribution']	['natural-language-processing']	[-9.84037220e-02 -1.37959599e-01 -3.67471308e-01 -4.24698859e-01 -3.92849922e-01 -6.60172343e-01 8.52185428e-01 1.60900325e-01 -6.88932955e-01 5.35333633e-01 2.37262055e-01 -4.35931832e-01 9.59935933e-02 -4.29740548e-01 -2.03164801e-01 -1.94825932e-01 4.06271070e-01 8.03178668e-01 -1.73607647e-01 3.87173891e-02 7.55987704e-01 5.06507754e-01 -1.43579519e+00 -1.77678801e-02 7.68965244e-01 5.01911998e-01 3.70501168e-02 8.72872353e-01 -6.33760750e-01 8.59905839e-01 -9.90658760e-01 -5.86023569e-01 -2.58652329e-01 -5.42840898e-01 -8.33453894e-01 7.17550982e-03 5.24401367e-01 4.88688126e-02 -1.73749149e-01 8.90093982e-01 5.31983137e-01 6.13222159e-02 9.11754370e-01 -1.23984051e+00 -9.53410149e-01 9.26638544e-01 -2.61390477e-01 1.91371188e-01 2.97565907e-01 -4.07432377e-01 9.48717654e-01 -9.52811420e-01 4.28187013e-01 1.43802774e+00 6.44790769e-01 5.46282947e-01 -1.05344486e+00 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462f0d54-d73b-4ee0-af47-abb9898daade	fracture-detection-in-wrist-x-ray-images	2111.07355	null	https://arxiv.org/abs/2111.07355v3	https://arxiv.org/pdf/2111.07355v3.pdf	Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models	Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patients medical records and physical examination. The aim of this study is to perform fracture detection by use of deep learning on wrist Xray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R_CNN, FSAF, Faster R_CNN, Dynamic R_CNN and DCN deep learning based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospitals dataset of wrist Xray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, wrist fracture detection_combo (WFD_C). From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor. Code is available at https://github.com/fatihuysal88/wrist-d	['Fatih Mert', 'Boran Demirciler', 'Uğurhan Kutbay', 'Nil Tokgöz', 'Tolga Tolunay', 'Murat Çiçeklidağ', 'Ozan Peker', 'Fatih Uysal', 'Fırat Hardalaç']	2021-11-14	null	null	null	null	['medical-object-detection']	['computer-vision']	[-5.32584369e-01 -2.73043811e-01 1.11996368e-01 9.55111235e-02 -1.03049672e+00 -1.58297122e-01 -1.67254090e-01 -1.56034296e-02 -4.76355314e-01 6.62722409e-01 3.50610524e-01 -3.34387422e-01 -5.22333980e-01 -1.09758890e+00 -2.95350760e-01 -5.71800351e-01 -3.14562887e-01 7.53393531e-01 1.57478571e-01 -2.59403497e-01 9.10955220e-02 1.02235901e+00 -1.07559621e+00 4.72849667e-01 2.75763214e-01 6.86071754e-01 2.20878139e-01 8.09703529e-01 5.21274447e-01 6.75063968e-01 -4.94997084e-01 -5.26794009e-02 4.89342183e-01 -9.16894376e-02 -8.20084512e-01 -2.16987848e-01 -1.08346902e-01 -7.75973320e-01 -8.85880709e-01 4.79554653e-01 1.38083446e+00 2.47120366e-01 8.08127582e-01 -6.18157208e-01 -8.62074196e-01 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6b10b272-73fa-4620-8e80-71f3dc3c3323	zero-shot-robot-manipulation-from-passive	2302.02011	null	https://arxiv.org/abs/2302.02011v1	https://arxiv.org/pdf/2302.02011v1.pdf	Zero-Shot Robot Manipulation from Passive Human Videos	Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of learning task-specific behaviors or direct imitation of a human video, we develop a a framework for extracting agent-agnostic action representations from human videos, and then map it to the agent's embodiment during deployment. Our framework is based on predicting plausible human hand trajectories given an initial image of a scene. After training this prediction model on a diverse set of human videos from the internet, we deploy the trained model zero-shot for physical robot manipulation tasks, after appropriate transformations to the robot's embodiment. This simple strategy lets us solve coarse manipulation tasks like opening and closing drawers, pushing, and tool use, without access to any in-domain robot manipulation trajectories. Our real-world deployment results establish a strong baseline for action prediction information that can be acquired from diverse arbitrary videos of human activities, and be useful for zero-shot robotic manipulation in unseen scenes.	['Vikash Kumar', 'Shubham Tulsiani', 'Abhinav Gupta', 'Homanga Bharadhwaj']	2023-02-03	null	null	null	null	['robot-manipulation']	['robots']	[ 2.94812381e-01 3.86803001e-01 -3.54180336e-01 -4.41047251e-02 -1.13136977e-01 -6.23600245e-01 7.51527190e-01 -4.96316671e-01 -3.64364564e-01 6.37217343e-01 3.06664079e-01 2.02294946e-01 -1.20878875e-01 -2.68845737e-01 -1.09129465e+00 -2.76805550e-01 -4.19081748e-01 7.56503403e-01 4.70733166e-01 -4.66293365e-01 1.34841040e-01 8.36463988e-01 -1.58515823e+00 3.26208293e-01 2.17855036e-01 6.13004684e-01 7.30169058e-01 1.09468722e+00 4.96612668e-01 1.61859572e+00 -3.92852187e-01 -8.81125256e-02 6.44307852e-01 -3.10023993e-01 -1.04513025e+00 6.54978216e-01 2.55282577e-02 -1.15824592e+00 -1.10069847e+00 7.39495635e-01 -2.07035795e-01 6.06906772e-01 8.93265903e-01 -1.70885718e+00 -5.57480514e-01 6.53419852e-01 -1.38507515e-01 -1.88883778e-03 1.01178336e+00 1.11158228e+00 7.32704580e-01 -4.06494081e-01 1.29557621e+00 1.47844279e+00 3.41038495e-01 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0d5ffecb-3078-4cfa-83cd-c431a3fad78f	synthesized-feature-based-few-shot-class	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Cheraghian_Synthesized_Feature_Based_Few-Shot_Class-Incremental_Learning_on_a_Mixture_of_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Cheraghian_Synthesized_Feature_Based_Few-Shot_Class-Incremental_Learning_on_a_Mixture_of_ICCV_2021_paper.pdf	Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces	Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes to a well-trained base model in multiple training sessions with the restriction that only a few novel instances are available per class. While learning novel classes, FSCIL methods gradually forget base (old) class training and overfit to a few novel class samples. Existing approaches have addressed this problem by computing the class prototypes from the visual or semantic word vector domain. In this paper, we propose addressing this problem using a mixture of subspaces. Subspaces define the cluster structure of the visual domain and help to describe the visual and semantic domain considering the overall distribution of the data. Additionally, we propose to employ a variational autoencoder (VAE) to generate synthesized visual samples for augmenting pseudo-feature while learning novel classes incrementally. The combined effect of the mixture of subspaces and synthesized features reduces the forgetting and overfitting problem of FSCIL. Extensive experiments on three image classification datasets show that our proposed method achieves competitive results compared to state-of-the-art methods.	['Mehrtash Harandi', 'Lars Petersson', 'Christian Simon', 'Pengfei Fang', 'Sameera Ramasinghe', 'Shafin Rahman', 'Ali Cheraghian']	2021-01-01	null	null	null	iccv-2021-1	['few-shot-class-incremental-learning']	['methodology']	[ 3.30386251e-01 -2.22329739e-02 -3.02996218e-01 -4.35403526e-01 -5.04050910e-01 -3.75395358e-01 7.07070470e-01 5.94077557e-02 -3.85144055e-01 7.98908889e-01 -6.57228695e-04 4.16819334e-01 -1.64527893e-02 -7.51621783e-01 -8.11701775e-01 -7.29565501e-01 4.53390628e-01 5.15807211e-01 4.41728890e-01 1.22715116e-01 7.57021978e-02 3.54304731e-01 -2.11000800e+00 4.35764700e-01 9.28042233e-01 7.11356282e-01 4.60262060e-01 3.72831672e-01 -4.67498004e-01 6.86688483e-01 -5.50368547e-01 -1.70595542e-01 1.35836035e-01 -5.12910426e-01 -3.99117291e-01 6.95965707e-01 4.45354491e-01 -3.31367284e-01 -3.04650545e-01 9.97649848e-01 3.05198461e-01 7.97345281e-01 7.78975189e-01 -1.50888646e+00 -8.93185139e-01 4.27043706e-01 -3.51061642e-01 -7.30239972e-02 -3.32891420e-02 2.90614534e-02 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ec0f27c5-529b-422e-8a3e-83f7e4dffc4c	dagobah-table-and-graph-contexts-for	null	null	https://www.eurecom.fr/fr/publication/6842	http://ceur-ws.org/Vol-3103/paper2.pdf	DAGOBAH: Table and Graph Contexts for Eﬀicient Semantic Annotation of Tabular Data	In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables. In particular, we report promising results obtained in the SemTab 2021 challenge thanks to optimisations in lookup mechanisms and new techniques for studying the context of nodes in the target knowledge graph. We also present the deployment of DAGOBAH algorithms within the Orange company via the TableAnnotation API and a front-end DAGOBAH user interface. These two access methods enable to accelerate the adoption of Semantic Table Interpretation solutions within the company to meet industrial needs.	['Raphaël Troncy', 'Pierre Monnin', 'Thomas Labbé', 'Frédéric Deuzé', 'Yoan Chabot', 'Jixiong Liu', 'Viet-Phi Huynh']	2021-10-01	null	null	null	proceedings-of-the-semantic-web-challenge-on	['table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation']	['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-3.18621397e-02 8.07252824e-01 -1.99374348e-01 -5.68614781e-01 -5.13275206e-01 -9.10646319e-01 5.65656960e-01 7.62561083e-01 1.32143036e-01 6.11905098e-01 5.69879189e-02 -5.85707843e-01 -4.96479958e-01 -1.26789689e+00 -5.87295353e-01 3.70266646e-01 1.90668963e-02 1.23984766e+00 6.12947106e-01 -5.39165318e-01 2.84397721e-01 3.98001462e-01 -1.77267766e+00 8.91548216e-01 2.96322823e-01 1.42261577e+00 -2.47187227e-01 4.68919635e-01 -4.13468361e-01 7.76453853e-01 -4.40802634e-01 -6.89484954e-01 5.34025133e-01 -3.90262366e-03 -1.07641935e+00 -4.26878422e-01 4.05915260e-01 6.70276061e-02 2.24856019e-01 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c0ef0c81-5668-4505-886d-4d7bc0149d84	self-supervised-3d-human-pose-estimation-with	2108.07777	null	https://arxiv.org/abs/2108.07777v1	https://arxiv.org/pdf/2108.07777v1.pdf	Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry	We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To train our model, represented by a deep neural network, we propose a four-loss function learning algorithm, which does not require any 2D or 3D body pose ground-truth. The proposed loss functions make use of the multiple-view geometry to reconstruct 3D body pose estimates and impose body pose constraints across the camera views. Our approach utilizes all available camera views during training, while the inference is single-view. In our evaluations, we show promising performance on Human3.6M and HumanEva benchmarks, while we also present a generalization study on MPI-INF-3DHP dataset, as well as several ablation results. Overall, we outperform all self-supervised learning methods and reach comparable results to supervised and weakly-supervised learning approaches. Our code and models are publicly available	['Vasileios Belagiannis', 'Ulrich Kressel', 'Julian Wiederer', 'Arij Bouazizi']	2021-08-17	null	null	null	null	['weakly-supervised-3d-human-pose-estimation']	['computer-vision']	[-2.23025933e-01 4.09159094e-01 -4.10859227e-01 -5.21167397e-01 -8.30088496e-01 -3.94850731e-01 3.39956135e-01 -2.37146810e-01 -5.81825852e-01 6.02187157e-01 3.01523596e-01 3.85069758e-01 3.44556421e-01 -3.57704937e-01 -1.05567229e+00 -3.71704638e-01 -1.50635228e-01 1.02265906e+00 -5.23570785e-03 5.82714379e-02 -4.05952185e-01 1.11871131e-01 -1.08994818e+00 -1.19007982e-01 1.54517978e-01 8.68510067e-01 -4.21795964e-01 8.42844307e-01 9.01821613e-01 4.84208345e-01 -2.63815075e-01 -6.39116764e-01 4.95500237e-01 -2.60007471e-01 -9.00809646e-01 6.07436180e-01 1.14380407e+00 -7.28596091e-01 -3.13445181e-01 5.40168881e-01 9.12147164e-01 -1.68532040e-02 5.61806858e-01 -1.19095123e+00 3.46168429e-02 3.99803519e-02 -7.52227604e-01 -1.79526687e-01 8.43764544e-01 1.01284452e-01 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814b4cbb-f30c-43de-b86b-85599e37245e	egocentric-videoconferencing	2107.03109	null	https://arxiv.org/abs/2107.03109v1	https://arxiv.org/pdf/2107.03109v1.pdf	Egocentric Videoconferencing	We introduce a method for egocentric videoconferencing that enables hands-free video calls, for instance by people wearing smart glasses or other mixed-reality devices. Videoconferencing portrays valuable non-verbal communication and face expression cues, but usually requires a front-facing camera. Using a frontal camera in a hands-free setting when a person is on the move is impractical. Even holding a mobile phone camera in the front of the face while sitting for a long duration is not convenient. To overcome these issues, we propose a low-cost wearable egocentric camera setup that can be integrated into smart glasses. Our goal is to mimic a classical video call, and therefore, we transform the egocentric perspective of this camera into a front facing video. To this end, we employ a conditional generative adversarial neural network that learns a transition from the highly distorted egocentric views to frontal views common in videoconferencing. Our approach learns to transfer expression details directly from the egocentric view without using a complex intermediate parametric expressions model, as it is used by related face reenactment methods. We successfully handle subtle expressions, not easily captured by parametric blendshape-based solutions, e.g., tongue movement, eye movements, eye blinking, strong expressions and depth varying movements. To get control over the rigid head movements in the target view, we condition the generator on synthetic renderings of a moving neutral face. This allows us to synthesis results at different head poses. Our technique produces temporally smooth video-realistic renderings in real-time using a video-to-video translation network in conjunction with a temporal discriminator. We demonstrate the improved capabilities of our technique by comparing against related state-of-the art approaches.	['Christian Theobalt', 'Vladislav Golyanik', 'Ayush Tewari', 'Hans-Peter Seidel', 'Matthias Nießner', 'Justus Thies', 'Mohit Mendiratta', 'Mohamed Elgharib']	2021-07-07	null	null	null	null	['face-reenactment']	['computer-vision']	[ 3.69535238e-01 1.93485841e-01 2.89466470e-01 -2.38745585e-01 -4.18770552e-01 -7.34902740e-01 5.33848464e-01 -1.16977394e+00 -9.09182802e-02 5.75557053e-01 2.43471712e-01 8.61101523e-02 3.42802763e-01 -3.97861242e-01 -8.74716759e-01 -8.35253060e-01 3.33015382e-01 9.79909003e-02 -1.52926564e-01 -2.08062395e-01 -1.47654623e-01 6.24241412e-01 -1.80435681e+00 3.38128299e-01 3.79129797e-01 8.62219155e-01 -5.10473177e-02 1.02485442e+00 3.26139927e-01 5.50661504e-01 -5.87909341e-01 -7.36283600e-01 2.81691849e-01 -3.93339455e-01 -1.96811542e-01 5.62929690e-01 8.06815743e-01 -7.39040613e-01 -4.80393529e-01 7.29951024e-01 7.35849857e-01 1.60848096e-01 3.19169730e-01 -1.44217145e+00 -2.99293399e-01 -4.44796160e-02 -5.78658521e-01 -3.02410543e-01 1.21354592e+00 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121873aa-0112-465c-81e3-0ace3421e74d	gumbel-attention-for-multi-modal-machine	2103.08862	null	https://arxiv.org/abs/2103.08862v2	https://arxiv.org/pdf/2103.08862v2.pdf	Gumbel-Attention for Multi-modal Machine Translation	Multi-modal machine translation (MMT) improves translation quality by introducing visual information. However, the existing MMT model ignores the problem that the image will bring information irrelevant to the text, causing much noise to the model and affecting the translation quality. This paper proposes a novel Gumbel-Attention for multi-modal machine translation, which selects the text-related parts of the image features. Specifically, different from the previous attention-based method, we first use a differentiable method to select the image information and automatically remove the useless parts of the image features. Experiments prove that our method retains the image features related to the text, and the remaining parts help the MMT model generates better translations.	['Tiejun Zhao', 'Hailong Cao', 'Pengbo Liu']	2021-03-16	null	null	null	null	['multimodal-machine-translation']	['natural-language-processing']	[ 1.42821714e-01 5.96516989e-02 -4.13817704e-01 -6.15970939e-02 -8.82837772e-01 -3.68819237e-01 5.88635445e-01 -3.72524709e-01 -2.21835330e-01 6.58787847e-01 5.08075535e-01 -2.48369858e-01 1.89604878e-01 -5.28890967e-01 -1.00441909e+00 -8.25128078e-01 8.66939247e-01 3.70159388e-01 -1.09678041e-02 -4.45713788e-01 1.38867855e-01 2.21374750e-01 -9.69381571e-01 5.87986827e-01 1.18819904e+00 5.91822088e-01 4.88822728e-01 8.70786011e-02 -3.83223176e-01 4.76387411e-01 -6.65454090e-01 -5.86447299e-01 1.40708312e-01 -7.80452549e-01 -6.11505806e-01 2.56518900e-01 7.38647044e-01 -4.85729784e-01 -5.44214189e-01 1.31120634e+00 6.29395247e-01 -6.12552129e-02 6.18064284e-01 -1.18704855e+00 -1.59574986e+00 4.21849787e-01 -8.42258096e-01 1.66195482e-01 2.23052144e-01 3.05604432e-02 7.52867162e-01 -1.43940878e+00 9.23470140e-01 1.72546673e+00 2.81903505e-01 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dd70c42e-659b-40b8-92d0-3d382f2d1a3d	a-causal-lens-for-controllable-text-1	2201.09119	null	https://arxiv.org/abs/2201.09119v1	https://arxiv.org/pdf/2201.09119v1.pdf	A Causal Lens for Controllable Text Generation	Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two problems separately, and developed different conditional models which, however, are prone to producing biased text (e.g., various gender stereotypes). This paper proposes to formulate controllable text generation from a principled causal perspective which models the two tasks with a unified framework. A direct advantage of the causal formulation is the use of rich causality tools to mitigate generation biases and improve control. We treat the two tasks as interventional and counterfactual causal inference based on a structural causal model, respectively. We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on a small fraction of data. Experiments show significant superiority of the causal approach over previous conditional models for improved control accuracy and reduced bias.	['Li Erran Li', 'Zhiting Hu']	2022-01-22	a-causal-lens-for-controllable-text	http://proceedings.neurips.cc/paper/2021/hash/d0f5edad9ac19abed9e235c0fe0aa59f-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/d0f5edad9ac19abed9e235c0fe0aa59f-Paper.pdf	neurips-2021-12	['text-attribute-transfer']	['natural-language-processing']	[ 8.26337039e-01 6.71231151e-01 -7.46354640e-01 -4.11528498e-01 -6.26096129e-01 -3.37675780e-01 1.01749372e+00 1.47937089e-01 4.13244627e-02 1.37961411e+00 7.06373334e-01 -3.85394275e-01 -2.13722125e-01 -1.11837590e+00 -8.76228333e-01 -4.88879681e-01 2.88751096e-01 3.46531540e-01 -5.08592010e-01 7.39080831e-02 2.63696104e-01 -1.32456496e-01 -1.41127551e+00 6.61180541e-02 1.64116800e+00 1.90355062e-01 -4.18538898e-02 3.32412153e-01 9.50949192e-02 7.85790801e-01 -4.25818056e-01 -6.61736786e-01 -1.29140466e-01 -5.39535403e-01 -7.23814130e-01 -2.17866108e-01 3.17711085e-01 -3.46314728e-01 7.45044872e-02 1.04519916e+00 5.06197453e-01 -3.20545621e-02 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c44f6f8b-3e73-4980-a312-eaefe169e9f1	what-is-missing-in-deep-music-generation-a	2209.00182	null	https://arxiv.org/abs/2209.00182v1	https://arxiv.org/pdf/2209.00182v1.pdf	What is missing in deep music generation? A study of repetition and structure in popular music	Structure is one of the most essential aspects of music, and music structure is commonly indicated through repetition. However, the nature of repetition and structure in music is still not well understood, especially in the context of music generation, and much remains to be explored with Music Information Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and American) illustrate important music construction principles: (1) structure exists at multiple hierarchical levels, (2) songs use repetition and limited vocabulary so that individual songs do not follow general statistics of song collections, (3) structure interacts with rhythm, melody, harmony, and predictability, and (4) over the course of a song, repetition is not random, but follows a general trend as revealed by cross-entropy. These and other findings offer challenges as well as opportunities for deep-learning music generation and suggest new formal music criteria and evaluation methods. Music from recent music generation systems is analyzed and compared to human-composed music in our datasets, often revealing striking differences from a structural perspective.	['Roger B. Dannenberg', 'Huiran Yu', 'Shuqi Dai']	2022-09-01	null	null	null	null	['music-generation', 'music-generation', 'music-information-retrieval']	['audio', 'music', 'music']	[ 1.98531196e-01 -4.16076213e-01 -1.18658982e-01 1.33084804e-01 -3.26384276e-01 -8.97523880e-01 5.10863483e-01 9.91195664e-02 1.19285202e-02 4.29063618e-01 8.51632953e-01 2.94360459e-01 -7.65324950e-01 -5.76414347e-01 -3.92713130e-01 -7.13915467e-01 -2.95789838e-01 2.13699907e-01 -1.69573560e-01 -5.68441749e-01 4.09974098e-01 2.51618832e-01 -1.52420437e+00 2.91644007e-01 6.00606203e-01 7.57478774e-01 1.70599431e-01 5.13912976e-01 -1.51641771e-01 5.53801417e-01 -6.65641606e-01 -2.47807726e-01 3.87682229e-01 -1.01418042e+00 -7.59279490e-01 -1.24647627e-02 3.94105673e-01 9.75424498e-02 -7.12841898e-02 8.79522264e-01 6.10235572e-01 1.18620679e-01 6.26481771e-01 -4.97292817e-01 -6.40238464e-01 1.34291637e+00 -2.34618306e-01 -2.24117190e-01 3.19090426e-01 1.63775161e-01 1.77535629e+00 -4.91441697e-01 5.32213748e-01 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3a810454-57d7-4286-93d9-3b37e5ac2ff3	sture-spatial-temporal-mutual-representation	2201.06824	null	https://arxiv.org/abs/2201.06824v3	https://arxiv.org/pdf/2201.06824v3.pdf	STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking	Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics.	['Haidong Wang', 'Ming Wen', 'Ke Nai', 'Yaping Li', 'Zhiyong Li']	2022-01-18	null	null	null	null	['online-multi-object-tracking']	['computer-vision']	[ 1.42854095e-01 -5.52484870e-01 -3.74947757e-01 -5.50101660e-02 -4.56565261e-01 -4.90892380e-01 7.68107414e-01 1.61995143e-01 -6.24303579e-01 5.65204442e-01 -3.04992586e-01 3.52042615e-02 -2.76594311e-01 -6.97110832e-01 -8.57349873e-01 -8.88633549e-01 -1.84531108e-01 1.86245859e-01 7.64863312e-01 -1.26488969e-01 1.36928245e-01 3.74000996e-01 -1.96759081e+00 1.07343001e-02 6.88997447e-01 1.15490401e+00 4.94593740e-01 4.65867788e-01 -2.24802256e-01 7.83181906e-01 -5.36200225e-01 -1.05181329e-01 3.71363908e-01 -2.87597835e-01 -9.01781470e-02 -2.47570008e-01 8.49334359e-01 6.69016987e-02 -7.79356539e-01 1.33486342e+00 2.25003392e-01 1.98951319e-01 5.57124257e-01 -1.74379838e+00 -6.08077765e-01 3.93862367e-01 -7.70585597e-01 5.43717623e-01 3.52580734e-02 2.66247809e-01 9.99219120e-01 -6.88500822e-01 4.96169865e-01 1.19384062e+00 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26bccad5-5453-4ed7-a36a-43cd64075fed	stereovae-a-lightweight-stereo-matching	2305.11566	null	https://arxiv.org/abs/2305.11566v2	https://arxiv.org/pdf/2305.11566v2.pdf	StereoVAE: A lightweight stereo matching system through embedded GPUs	We present a lightweight system for stereo matching through embedded GPUs. It breaks the trade-off between accuracy and processing speed in stereo matching, enabling our embedded system to further improve the matching accuracy while ensuring real-time processing. The main idea of our method is to construct a tiny neural network based on variational auto-encoder (VAE) to upsample and refinement a small size of coarse disparity map, which is first generated by a traditional matching method. The proposed hybrid structure cannot only bring the advantage of traditional methods in terms of computational complexity, but also ensure the matching accuracy under the impact of neural network. Extensive experiments on the KITTI 2015 benchmark demonstrate that our tiny system exhibits high robustness in improving the accuracy of the coarse disparity maps generated by different algorithms, while also running in real-time on embedded GPUs.	['Miyazaki Jun', 'Yun Li', 'Xin Liu', 'Xin Xu', 'Xiang Li', 'Qiong Chang']	2023-05-19	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 8.14784840e-02 -2.33282939e-01 3.34675521e-01 -1.17337607e-01 -5.45529127e-01 -3.97735760e-02 3.91145259e-01 -1.06443852e-01 -5.52546561e-01 3.91675830e-01 1.79602895e-02 -3.02615017e-01 4.41378444e-01 -1.38193417e+00 -9.09765840e-01 -6.01568818e-01 2.70064265e-01 5.93262166e-02 3.68074387e-01 -2.66449839e-01 2.48165935e-01 2.16468170e-01 -2.08504915e+00 9.76855755e-02 1.18151081e+00 1.41481304e+00 1.42595008e-01 6.23727381e-01 3.56550030e-02 6.46792293e-01 -1.39735043e-01 -4.11391288e-01 8.84033024e-01 -7.55949393e-02 -1.68974593e-01 -1.93430066e-01 9.94227767e-01 -7.75094628e-01 -6.06640637e-01 1.33909142e+00 7.68605053e-01 -5.44286817e-02 2.29384705e-01 -9.48561311e-01 -2.23418906e-01 2.61324376e-01 -6.91366374e-01 -1.26238793e-01 1.21785797e-01 3.52350980e-01 5.58518767e-01 -8.09777498e-01 3.54546756e-01 1.20475101e+00 1.13934553e+00 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9ede3bab-276e-4833-bc55-99a8f54b64a8	representation-learning-on-unit-ball-with-3d	1912.01454	null	https://arxiv.org/abs/1912.01454v1	https://arxiv.org/pdf/1912.01454v1.pdf	Representation Learning on Unit Ball with 3D Roto-Translational Equivariance	Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learning in deep neural networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere ($\mathbb{S}^2$) or a unit ball ($\mathbb{B}^3$)---entails unique challenges. In this work, we propose a novel `\emph{volumetric convolution}' operation that can effectively model and convolve arbitrary functions in $\mathbb{B}^3$. We develop a theoretical framework for \emph{volumetric convolution} based on Zernike polynomials and efficiently implement it as a differentiable and an easily pluggable layer in deep networks. By construction, our formulation leads to the derivation of a novel formula to measure the symmetry of a function in $\mathbb{B}^3$ around an arbitrary axis, that is useful in function analysis tasks. We demonstrate the efficacy of proposed volumetric convolution operation on one viable use case i.e., 3D object recognition.	['Sameera Ramasinghe', 'Nick Barnes', 'Salman Khan', 'Stephen Gould']	2019-11-30	null	null	null	null	['3d-object-recognition']	['computer-vision']	[-3.17739174e-02 5.17543964e-02 7.09383428e-01 -4.37596738e-01 -9.20528769e-02 -6.35617256e-01 2.80109286e-01 1.76030095e-03 -3.56566876e-01 5.43459117e-01 -3.63075972e-01 -6.18407011e-01 -5.21071494e-01 -1.35777342e+00 -9.16562200e-01 -8.72179687e-01 -5.54560363e-01 1.15099102e-01 7.56512806e-02 -3.87537986e-01 2.18554899e-01 1.13423741e+00 -1.50489974e+00 1.09878801e-01 6.48792326e-01 1.75071764e+00 -1.90215513e-01 5.77527463e-01 -1.97641537e-01 5.66163659e-01 -6.24105036e-01 -5.10410845e-01 4.36547488e-01 -3.93666178e-02 -9.09455001e-01 -2.17499748e-01 4.44145471e-01 -8.51937458e-02 -5.23587942e-01 1.10895967e+00 2.74171054e-01 4.23839629e-01 1.09667146e+00 -9.58405077e-01 -9.15645719e-01 1.88647151e-01 -1.81878120e-01 3.24325502e-01 -4.22583595e-02 -2.12553471e-01 1.05868590e+00 -1.06671321e+00 2.15365276e-01 1.01427102e+00 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16576f27-e6a3-4a9b-9dda-279a26758d94	u-shape-transformer-for-underwater-image	2111.11843	null	https://arxiv.org/abs/2111.11843v6	https://arxiv.org/pdf/2111.11843v6.pdf	U-shape Transformer for Underwater Image Enhancement	The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we constructed a large-scale underwater image (LSUI) dataset including 5004 image pairs, and reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority.	['Liheng Bian', 'Chunli Zhu', 'Lintao Peng']	2021-11-23	null	null	null	null	['underwater-image-restoration', 'uie']	['computer-vision', 'computer-vision']	[ 1.67858958e-01 -4.10775274e-01 1.06235683e+00 -3.83536518e-01 -5.29179633e-01 2.18027066e-02 9.59896967e-02 -1.70259371e-01 -8.70280623e-01 6.13373458e-01 3.01903427e-01 1.96848720e-01 -3.03940028e-01 -9.80939567e-01 -6.89043641e-01 -1.24414670e+00 -2.45894521e-01 -5.98143220e-01 4.89974797e-01 -6.50377691e-01 1.97020382e-01 5.43495603e-02 -1.55356324e+00 -7.17606470e-02 1.37919021e+00 1.21580553e+00 4.74976987e-01 3.88869345e-01 -1.05680779e-01 5.93303621e-01 -3.13400179e-01 -4.80133206e-01 4.57524061e-01 -4.56421793e-01 -8.71627331e-02 -1.07746169e-01 5.14446914e-01 -8.17732811e-01 -4.96049613e-01 1.47911370e+00 1.09957755e+00 1.41611174e-01 2.04670444e-01 -9.40178573e-01 -6.42429352e-01 2.50108212e-01 -6.63824022e-01 1.60302699e-01 -1.48568496e-01 8.03577974e-02 5.72001457e-01 -1.17466414e+00 2.35626861e-01 1.18482029e+00 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bb9f5c71-bc63-4772-9978-2eeeaecaa588	motion-state-alignment-for-video-semantic	2304.08820	null	https://arxiv.org/abs/2304.08820v1	https://arxiv.org/pdf/2304.08820v1.pdf	Motion-state Alignment for Video Semantic Segmentation	In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.	['Junfeng Luo', 'Shuaibin Zhang', 'Ruihong Yin', 'Jinming Su']	2023-04-18	null	null	null	null	['video-semantic-segmentation']	['computer-vision']	[ 1.68157771e-01 -4.08076882e-01 -5.65806389e-01 -4.03079927e-01 -3.46719056e-01 -2.34524652e-01 3.48136991e-01 -2.37438023e-01 -5.04792035e-01 3.38885665e-01 -4.38137539e-02 1.22693114e-01 -1.39689624e-01 -8.41536701e-01 -4.11728293e-01 -9.27963316e-01 2.17873901e-01 1.36901215e-01 9.83051777e-01 -5.56809381e-02 3.13597798e-01 1.26973674e-01 -1.51443779e+00 6.53655753e-02 1.00906527e+00 1.29518545e+00 6.49599791e-01 1.10453457e-01 -6.47113383e-01 6.64027631e-01 -2.31269330e-01 2.10492373e-01 2.17191845e-01 -4.61378276e-01 -9.68338966e-01 4.81659949e-01 2.36344606e-01 -3.93135399e-01 -4.55529034e-01 1.24264467e+00 2.85892218e-01 3.45363468e-01 -2.72814296e-02 -1.20772564e+00 4.38042358e-03 4.35444474e-01 -6.70064867e-01 4.51313734e-01 5.66873103e-02 2.38517076e-01 9.65904891e-01 -4.84810680e-01 8.53806078e-01 1.29023600e+00 3.80408078e-01 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a4b23fc8-69e7-426c-8919-a89352dd3b23	a-worst-case-performance-optimization-based	1908.11470	null	http://arxiv.org/abs/1908.11470v1	http://arxiv.org/pdf/1908.11470v1.pdf	A Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO	This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder's output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-to-interference-plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We then adopt a worst-case design approach to protect the users' intended symbols and the targeted constructive interference with a desired level of confidence. Due to the non-convexity of the relaxed robust formulation, we propose an iterative algorithm based on the block coordinate ascent-descent method. We show through simulation results that the proposed robust design is flexible in the sense that the CI constraints can be relaxed so as to keep a desirable balance between achievable rate and power consumption. Remarkably, the new formulation yields more energy-efficient solutions for appropriate choices of the penalty parameter, compared to the original problem.	[]	2019-08-29	null	null	null	null	['robust-design']	['miscellaneous']	[ 6.18022799e-01 3.25310886e-01 -4.22810763e-01 8.16145614e-02 -8.46208811e-01 -4.13614124e-01 1.22322358e-01 -1.55178428e-01 -3.10339123e-01 8.52572441e-01 1.81403503e-01 -7.57375360e-01 -3.71626914e-01 -5.78166783e-01 -5.58223665e-01 -1.13338411e+00 -1.30086631e-01 -5.85658908e-01 -5.79280972e-01 -2.00340673e-01 -9.57184732e-02 2.42705435e-01 -5.91644526e-01 -4.85241592e-01 1.00324118e+00 1.12985671e+00 3.67236376e-01 5.96437454e-01 8.21291864e-01 3.91011387e-01 -4.52119321e-01 -2.92374313e-01 4.68698323e-01 -8.19790244e-01 -1.39315411e-01 4.61246163e-01 -3.65677297e-01 -2.03128606e-01 -7.69674659e-01 1.19379437e+00 7.25688159e-01 -6.55183792e-02 4.27620649e-01 -1.03374350e+00 -3.95259023e-01 2.95289487e-01 -8.25144470e-01 1.28223076e-01 1.97933661e-03 -6.35130763e-01 9.52198207e-01 -9.30297494e-01 1.51116580e-01 9.76872802e-01 2.93766886e-01 2.39871010e-01 -1.02712798e+00 -5.00446677e-01 -5.32677509e-02 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-4.21037942e-01 4.66365635e-01 -2.63678223e-01 -9.75619555e-02 -2.82912225e-01]	[6.156276226043701, 1.4227195978164673]
afca5732-8fd8-4156-b725-5ab77027157b	black-box-variational-inference-with-a	2304.05527	null	https://arxiv.org/abs/2304.05527v1	https://arxiv.org/pdf/2304.05527v1.pdf	Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box	Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty estimates of mean-field variational Bayes (MFVB). We introduce ``deterministic ADVI'' (DADVI) to address these issues. DADVI replaces the intractable MFVB objective with a fixed Monte Carlo approximation, a technique known in the stochastic optimization literature as the ``sample average approximation'' (SAA). By optimizing an approximate but deterministic objective, DADVI can use off-the-shelf second-order optimization, and, unlike standard mean-field ADVI, is amenable to more accurate posterior linear response (LR) covariance estimates. In contrast to existing worst-case theory, we show that, on certain classes of common statistical problems, DADVI and the SAA can perform well with relatively few samples even in very high dimensions, though we also show that such favorable results cannot extend to variational approximations that are too expressive relative to mean-field ADVI. We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.	['Tamara Broderick', 'Martin Ingram', 'Ryan Giordano']	2023-04-11	null	null	null	null	['probabilistic-programming', 'stochastic-optimization']	['methodology', 'methodology']	[-8.99827927e-02 -3.57152969e-02 -1.30983200e-02 -4.38153863e-01 -1.64632618e+00 -7.55193055e-01 8.16993654e-01 -2.64603555e-01 -3.87286842e-01 1.11257660e+00 6.82661310e-02 -2.85567880e-01 -5.43143630e-01 -5.68653941e-01 -7.94888616e-01 -9.93267298e-01 -2.95573846e-02 9.65802252e-01 8.81493688e-02 1.99326456e-01 1.89439863e-01 2.28268206e-01 -1.21950293e+00 -4.28092778e-01 9.31007683e-01 7.60258377e-01 4.18422632e-02 6.25552416e-01 7.75749981e-02 2.71339208e-01 -3.49109799e-01 -3.70664626e-01 -9.19916928e-02 -1.37498558e-01 -5.82873583e-01 -5.39644480e-01 3.84203315e-01 -4.57337052e-01 -5.11289164e-02 1.14332557e+00 5.03946662e-01 4.77406979e-01 1.30911362e+00 -1.22963583e+00 -6.91246092e-01 6.72591925e-01 -7.99962103e-01 2.08829299e-01 3.08355540e-01 2.21792534e-01 1.17281044e+00 -1.02933824e+00 4.95673507e-01 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1.99150175e-01 -1.10852861e+00 -1.29538298e+00 -9.80803132e-01 -9.74678636e-01 -1.33931935e-01 3.31268132e-01 -1.08101499e+00 -7.80112147e-01 -4.66337353e-01]	[6.934023380279541, 3.9871633052825928]
fc25ab57-3699-4e81-b410-3a2d4ef46517	salypath360-saliency-and-scanpath-prediction	2201.00096	null	https://arxiv.org/abs/2201.00096v1	https://arxiv.org/pdf/2201.00096v1.pdf	SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images	This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by an attention module to generate representative saliency maps. In addition, an auxiliary network is employed to generate probable viewport center fixation points through the SoftArgMax function. The latter allows to derive fixation points from feature maps. To take advantage of the scanpath prediction, an adaptive joint probability distribution model is then applied to construct the final unbiased saliency map by leveraging the encoder decoder-based saliency map and the scanpath-based saliency heatmap. The proposed framework was evaluated in terms of saliency and scanpath prediction, and the results were compared to state-of-the-art methods on Salient360! dataset. The results showed the relevance of our framework and the benefits of such architecture for further omnidirectional visual attention prediction tasks.	['Mohamed Sayeh', 'Aladine Chetouani', 'Marouane Tliba', 'Mohamed Amine Kerkouri']	2022-01-01	null	null	null	null	['scanpath-prediction']	['computer-vision']	[ 3.69278103e-01 3.81854564e-01 -1.20050155e-01 -6.28957331e-01 -5.15235364e-01 6.25089258e-02 5.75466394e-01 -1.68160707e-01 -3.05249065e-01 7.20843673e-01 4.34271842e-01 -6.73400983e-02 9.40455422e-02 -6.66844964e-01 -1.11782420e+00 -7.59047985e-01 2.14231506e-01 3.02182487e-03 3.67431045e-01 -1.02123566e-01 8.23665917e-01 2.41862297e-01 -2.24048996e+00 3.83986719e-02 1.03185093e+00 1.08252728e+00 1.18716609e+00 5.21250963e-01 1.84650362e-01 5.82077742e-01 -2.61096269e-01 -8.66951346e-02 9.38667506e-02 -1.17993377e-01 -5.05229890e-01 -2.57440120e-01 3.74793082e-01 -2.42033154e-01 -1.83436349e-01 1.17104185e+00 7.58753598e-01 2.07065299e-01 4.71882641e-01 -1.05057704e+00 -8.26926708e-01 4.59707081e-01 -6.55372083e-01 5.29353261e-01 2.04019472e-01 2.26479933e-01 1.11881542e+00 -1.07269144e+00 6.04190707e-01 9.64383364e-01 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6.61807716e-01 -1.40870142e+00 -8.95382613e-02 -1.01789331e+00 -2.50083148e-01 1.77865803e-01 3.66742998e-01 -8.75345469e-01 -1.09172218e-01 -1.18878901e-01]	[9.870128631591797, -0.3573067784309387]
16d91683-bebc-4ca2-bb62-e29a0a2718db	joint-extraction-of-entities-and-relations	1706.05075	null	http://arxiv.org/abs/1706.05075v1	http://arxiv.org/pdf/1706.05075v1.pdf	Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme	Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.	['Peng Zhou', 'Feng Wang', 'Bo Xu', 'Yuexing Hao', 'Suncong Zheng', 'Hongyun Bao']	2017-06-07	joint-extraction-of-entities-and-relations-1	https://aclanthology.org/P17-1113	https://aclanthology.org/P17-1113.pdf	acl-2017-7	['joint-entity-and-relation-extraction']	['natural-language-processing']	[-2.68583328e-01 4.16002989e-01 -3.06712449e-01 -4.12643820e-01 -6.67191029e-01 -5.13805270e-01 5.11274517e-01 2.00199291e-01 -6.46907032e-01 9.83507276e-01 3.58378172e-01 -1.17554277e-01 -1.41317531e-01 -7.37818718e-01 -3.72952372e-01 -3.02530587e-01 -2.15745151e-01 5.51260114e-01 6.39938653e-01 -7.47371241e-02 -3.20839658e-02 2.37734661e-01 -9.21409607e-01 2.81878054e-01 8.74833882e-01 7.73087323e-01 -1.74261127e-02 2.24143282e-01 -3.75227571e-01 9.87201750e-01 -4.39571857e-01 -9.18910980e-01 -1.14129812e-01 -1.42882392e-01 -1.40706265e+00 -3.39242071e-01 -1.61142468e-01 -3.09211425e-02 -1.70011744e-01 1.04199660e+00 3.67524773e-01 -1.49618924e-01 5.87679267e-01 -1.21196353e+00 -6.52052820e-01 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08af5b99-de08-4592-842d-0303e9d57735	a-survey-of-deep-learning-for-low-shot-object	2112.02814	null	https://arxiv.org/abs/2112.02814v3	https://arxiv.org/pdf/2112.02814v3.pdf	A Survey of Deep Learning for Low-Shot Object Detection	Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task. Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and analyze them systematically, comprising some extensional topics of LSOD (semi-supervised LSOD, weakly-supervised LSOD and incremental LSOD). Then, we indicate the pros and cons of current LSOD methods with a comparison of their performance. Finally, we discuss the challenges and promising directions of LSOD to provide guidance for future works.	['Mengqi Xue', 'Mingli Song', 'Jie Song', 'Haofei Zhang', 'Qihan Huang']	2021-12-06	null	null	null	null	['one-shot-object-detection', 'zero-shot-object-detection']	['computer-vision', 'computer-vision']	[ 2.85251170e-01 -1.53326720e-01 -3.34480375e-01 -9.06497613e-02 -6.76347196e-01 -1.01705179e-01 5.76148331e-01 1.23356320e-01 -3.17089230e-01 3.60289693e-01 -2.62126982e-01 2.50284255e-01 -2.17632622e-01 -7.15114713e-01 -2.25833982e-01 -8.23125958e-01 4.39449176e-02 1.57415271e-01 9.37945902e-01 6.48118779e-02 5.48983626e-02 4.13348317e-01 -2.03069401e+00 -6.79635033e-02 4.95967537e-01 1.06892967e+00 4.77616847e-01 7.25627244e-01 -3.35946262e-01 8.35408092e-01 -6.48091376e-01 -2.27889672e-01 2.41792157e-01 -4.61481303e-01 -5.49748003e-01 2.28180677e-01 4.61677343e-01 -4.22932029e-01 -2.68383116e-01 1.29385495e+00 8.87327075e-01 2.63067901e-01 7.80649722e-01 -1.57210636e+00 -4.90906805e-01 3.49189103e-01 -6.09796762e-01 6.57240510e-01 -3.82612571e-02 1.60658523e-01 9.21841204e-01 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38711df9-f2c4-4459-9f22-97f7500c5741	generalized-expectation-maximization	2305.13880	null	https://arxiv.org/abs/2305.13880v1	https://arxiv.org/pdf/2305.13880v1.pdf	Generalized Expectation Maximization Framework for Blind Image Super Resolution	Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels. However, these methods mostly require an independent step to estimate the blur kernel, leading to error accumulation between steps. We propose an end-to-end learning framework for the blind SISR problem, which enables image restoration within a unified Bayesian framework with either full- or semi-supervision. The proposed method, namely SREMN, integrates learning techniques into the generalized expectation-maximization (GEM) algorithm and infers HR images from the maximum likelihood estimation (MLE). Extensive experiments show the superiority of the proposed method with comparison to existing work and novelty in semi-supervised learning.	['Yuan Shen', 'Zhiming Wang', 'Yuxiao Li']	2023-05-23	null	null	null	null	['image-super-resolution', 'image-restoration']	['computer-vision', 'computer-vision']	[ 3.69109362e-01 -2.67488718e-01 -9.11581144e-02 -4.28689837e-01 -1.35454810e+00 -8.13149512e-02 4.54265147e-01 -7.87206113e-01 -2.21006736e-01 8.90638471e-01 4.15466934e-01 -8.39510337e-02 -4.49333906e-01 -2.98332155e-01 -5.67875087e-01 -8.48461628e-01 2.86497116e-01 7.19534531e-02 -1.54880562e-03 2.95367479e-01 3.02578270e-01 2.68921196e-01 -1.32991028e+00 2.11493880e-01 1.15782738e+00 7.11538255e-01 8.16711605e-01 7.43336380e-01 4.24378395e-01 1.29284406e+00 -2.54946977e-01 -7.64247701e-02 3.12781721e-01 -4.68274266e-01 -6.42009020e-01 5.07771492e-01 6.03784144e-01 -7.21805453e-01 -6.37781262e-01 1.31249714e+00 5.55490971e-01 1.43783376e-01 5.98445356e-01 -5.73383391e-01 -1.16639912e+00 3.49996239e-01 -9.15906429e-01 2.88945228e-01 5.31990409e-01 -5.91787323e-02 5.37345827e-01 -1.20471036e+00 2.52418607e-01 1.31277204e+00 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64af0f74-27d6-4f2c-b47d-7b37ddef7aad	hierarchical-graph-matching-networks-for-deep-1	2007.04395	null	https://arxiv.org/abs/2007.04395v4	https://arxiv.org/pdf/2007.04395v4.pdf	Multilevel Graph Matching Networks for Deep Graph Similarity Learning	While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase.	['Chunming Wu', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling', 'Shouling Ji', 'Fangli Xu', 'Alex X. Liu', 'Tengfei Ma']	2020-07-08	null	null	null	null	['graph-similarity', 'graph-regression']	['graphs', 'graphs']	[ 7.87806138e-03 2.93663502e-01 -1.39355257e-01 -1.89431995e-01 -1.70256376e-01 -4.20375705e-01 6.09851599e-01 7.45897412e-01 3.29718851e-02 1.53462887e-01 -1.73818931e-01 -3.00601721e-01 -2.12828502e-01 -1.19121742e+00 -7.27240086e-01 -5.88200033e-01 -5.43770790e-01 4.73609596e-01 3.44974875e-01 -1.92119196e-01 -7.20059276e-02 4.73777443e-01 -1.18621743e+00 -1.33138031e-01 7.79231727e-01 7.27850914e-01 5.28093986e-03 5.85982978e-01 6.18736669e-02 7.16444969e-01 -2.74096042e-01 -7.13021457e-01 3.29680860e-01 -4.12486166e-01 -6.34568453e-01 1.75651491e-01 9.43174958e-01 2.10102126e-01 -8.55913639e-01 1.29503906e+00 4.06889439e-01 2.66188920e-01 5.80653846e-01 -1.69167197e+00 -9.27645445e-01 7.93561935e-01 -7.94709980e-01 2.27756098e-01 4.07336891e-01 1.84347332e-02 1.59378541e+00 -4.44856644e-01 5.72605073e-01 1.37290561e+00 8.06949317e-01 1.64397016e-01 -1.15760398e+00 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1.03415227e+00 1.31988764e-01 -5.32472908e-01 -4.03598368e-01 -5.83533943e-01]	[7.183712482452393, 6.264597415924072]
cee00ab8-2476-4f46-b2e1-61ecbe5743ee	uierl-internal-external-representation	2306.08344	null	https://arxiv.org/abs/2306.08344v1	https://arxiv.org/pdf/2306.08344v1.pdf	UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement	Underwater image enhancement (UIE) is a meaningful but challenging task, and many learning-based UIE methods have been proposed in recent years. Although much progress has been made, these methods still exist two issues: (1) There exists a significant region-wise quality difference in a single underwater image due to the underwater imaging process, especially in regions with different scene depths. However, existing methods neglect this internal characteristic of underwater images, resulting in inferior performance; (2) Due to the uniqueness of the acquisition approach, underwater image acquisition tools usually capture multiple images in the same or similar scenes. Thus, the underwater images to be enhanced in practical usage are highly correlated. However, when processing a single image, existing methods do not consider the rich external information provided by the related images. There is still room for improvement in their performance. Motivated by these two aspects, we propose a novel internal-external representation learning (UIERL) network to better perform UIE tasks with internal and external information, simultaneously. In the internal representation learning stage, a new depth-based region feature guidance network is designed, including a region segmentation based on scene depth to sense regions with different quality levels, followed by a region-wise space encoder module. With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement. In the external representation learning stage, we first propose an external information extraction network to mine the rich external information in the related images. Then, internal and external features interact with each other via the proposed external-assist-internal module and internal-assist-e	['Yuan Hui', 'Yihan Yu', 'Liquan Shen', 'Zhengyong Wang']	2023-06-14	null	null	null	null	['image-enhancement', 'uie']	['computer-vision', 'computer-vision']	[ 3.95000517e-01 -1.56638965e-01 4.35622990e-01 -4.68836695e-01 -5.37004769e-01 -4.16695364e-02 8.58250856e-02 1.03127457e-01 -8.52364898e-01 4.56021130e-01 2.97273874e-01 3.63448888e-01 -2.50903428e-01 -1.12169337e+00 -4.59083468e-01 -8.12159598e-01 -1.56192034e-01 -3.56590420e-01 6.18923604e-01 -5.54276228e-01 3.58146399e-01 3.63173574e-01 -1.54227161e+00 -3.19857150e-02 1.09491265e+00 1.12804532e+00 7.99015105e-01 4.11562711e-01 -3.59951347e-01 6.76561892e-01 -4.83841628e-01 1.16795786e-02 4.33839172e-01 -6.82905793e-01 -3.90936852e-01 1.55165985e-01 3.94188687e-02 -7.14213789e-01 -5.84403515e-01 1.40335226e+00 7.77662337e-01 3.67568076e-01 3.95291299e-01 -7.77763247e-01 -3.27464104e-01 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dd93e307-b242-48d0-afa4-82a2e83c75ed	intrinsic-and-extrinsic-evaluation-of	null	null	https://aclanthology.org/W17-2624	https://aclanthology.org/W17-2624.pdf	Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams	Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these spatiotemporal changes but no general method exists to evaluate the quality of these representations. Previous work largely focused on qualitative evaluation, which we improve by proposing a set of visualizations that highlight changes in text representation over both space and time. We demonstrate usefulness of novel spatiotemporal representations to explore and characterize specific aspects of the corpus of tweets collected from European countries over a two-week period centered around the terrorist attacks in Brussels in March 2016. In addition, we quantitatively evaluate spatiotemporal representations by feeding them into a downstream classification task {--} event type prediction. Thus, our work is the first to provide both intrinsic (qualitative) and extrinsic (quantitative) evaluation of text representations for spatiotemporal trends.	['Lawrence Phillips', 'Kyle Shaffer', 'Dustin Arendt', 'Svitlana Volkova', 'Nathan Hodas']	2017-08-01	null	null	null	ws-2017-8	['type-prediction']	['computer-code']	[-5.16528338e-02 -3.61124545e-01 -1.26992211e-01 -2.41563246e-01 -5.42215466e-01 -8.98922384e-01 1.40372515e+00 1.27283442e+00 -5.64931393e-01 6.00854158e-01 9.30400789e-01 -3.03982288e-01 -2.04299152e-01 -8.61515641e-01 -2.14549750e-01 -2.30296239e-01 -6.55930519e-01 6.44409331e-03 1.70897886e-01 -5.10936558e-01 4.03436542e-01 6.67558193e-01 -1.50552332e+00 4.62139338e-01 3.67825866e-01 5.30490577e-01 -9.73095596e-02 5.86375952e-01 -5.95707417e-01 5.83812654e-01 -9.43945587e-01 3.24121676e-02 -2.32931003e-01 -2.82345355e-01 -6.34902000e-01 -4.61457342e-01 1.65984660e-01 3.66927505e-01 -6.06828213e-01 7.10555732e-01 3.04004431e-01 5.74619412e-01 7.03058183e-01 -8.90612006e-01 -7.47328103e-01 5.98265409e-01 -5.05851746e-01 1.07156312e+00 7.63906538e-01 2.82362960e-02 7.37757862e-01 -8.96768093e-01 1.10772610e+00 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195fd689-6aea-45d6-8d27-f3fd884a8a5f	deep-template-matching-for-offline	1811.06347	null	http://arxiv.org/abs/1811.06347v1	http://arxiv.org/pdf/1811.06347v1.pdf	Deep Template Matching for Offline Handwritten Chinese Character Recognition	Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this paper, we propose a novel method for learning siamese neural network which employ a special structure to predict the similarity between handwritten Chinese characters and template images. The optimization of siamese neural network can be treated as a simple binary classification problem. When the training process has been finished, the powerful discriminative features help us to generalize the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalization ability to the new classes that never appear in the training set.	['Huaxiang Lu', 'Qi Wu', 'Zhiyuan Li', 'Min Jin']	2018-11-15	null	null	null	null	['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character']	['computer-vision', 'natural-language-processing']	[ 1.33045018e-01 -6.57421231e-01 -4.55638394e-02 -5.19817173e-01 -3.29107225e-01 -5.08218527e-01 5.84494710e-01 -4.00083423e-01 -7.33127117e-01 7.72938609e-01 -2.10616618e-01 -9.08390880e-02 -6.25189394e-02 -4.99922186e-01 -4.72499460e-01 -9.13730383e-01 -1.30233169e-02 5.28738201e-01 1.39811754e-01 -1.86875060e-01 4.30697769e-01 1.08077598e+00 -1.10859203e+00 2.52269566e-01 1.02118874e+00 9.80090201e-01 4.92504627e-01 5.26795149e-01 -1.24849394e-01 1.05422044e+00 -7.51391709e-01 -4.07039702e-01 3.48750055e-01 -4.93110299e-01 -6.63309693e-01 3.33243251e-01 3.72307688e-01 -4.35403436e-02 -8.11861098e-01 1.01069355e+00 2.46477216e-01 3.47972929e-01 7.82728553e-01 -7.52411067e-01 -1.02282155e+00 4.24592465e-01 -3.50013703e-01 2.18609840e-01 -3.32116298e-02 -1.36733606e-01 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7.70678341e-01 9.42606091e-01 -1.74022287e-01 3.04738671e-01 9.88487303e-01 -6.35591626e-01 -1.05305457e+00 -9.35695231e-01 -8.82356882e-01 3.69708836e-01 3.25989097e-01 -1.71492055e-01 -2.11023703e-01 8.73022228e-02]	[11.848799705505371, 2.5567684173583984]
597badac-e6c9-43a5-868f-e8d2aa2c5d5b	towards-real-time-visual-tracking-with-graded	2206.08701	null	https://arxiv.org/abs/2206.08701v1	https://arxiv.org/pdf/2206.08701v1.pdf	Towards Real-Time Visual Tracking with Graded Color-names Features	MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking performance will be deteriorated. In this paper, we address the challenges above-mentioned by developing a tracking method that combines the background models and the graded features of color-names under the MeanShift framework. This method significantly improve performance in the above scenarios. In addition, it facilitates the balance between detection accuracy and detection speed. Experimental results demonstrate the validation of the proposed method.	['Xuemei Guo', 'Guoli Wang', 'Lin Li']	2022-06-17	null	null	null	null	['visual-tracking', 'real-time-visual-tracking']	['computer-vision', 'computer-vision']	[ 6.51180446e-02 -7.06619561e-01 -3.51810083e-02 4.23437208e-02 -1.15275748e-01 -5.37023604e-01 5.63341737e-01 -6.18613958e-02 -4.30632263e-01 5.39937496e-01 -4.30827409e-01 -9.75017920e-02 1.69422105e-01 -7.29324460e-01 -1.28238425e-01 -1.01796901e+00 1.97687984e-01 -2.12775506e-02 1.00411510e+00 4.33965698e-02 2.28176117e-01 7.17440069e-01 -1.50828052e+00 -2.13987067e-01 8.15510571e-01 1.08843458e+00 4.07787323e-01 2.81521440e-01 -2.78433591e-01 3.62609714e-01 -6.15571380e-01 -1.24926485e-01 4.89811987e-01 -3.62827480e-01 1.26396209e-01 2.19701171e-01 2.94188410e-01 -3.25860679e-01 -3.48314136e-01 1.47765839e+00 3.89964014e-01 1.30889550e-01 2.31744677e-01 -1.16611564e+00 -7.82341808e-02 1.89159155e-01 -1.12017083e+00 4.06837553e-01 -2.88445782e-02 -8.05122107e-02 2.81451613e-01 -9.38264072e-01 3.83671403e-01 1.21761465e+00 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a36a9805-2b6b-4b4b-bf7f-c3b9789ef3a4	generalizability-vs-robustness-adversarial	1804.00504	null	http://arxiv.org/abs/1804.00504v1	http://arxiv.org/pdf/1804.00504v1.pdf	Generalizability vs. Robustness: Adversarial Examples for Medical Imaging	In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.	['Fernando Navarro', 'Sailesh Conjeti', 'Magdalini Paschali', 'Nassir Navab']	2018-03-23	null	null	null	null	['skin-lesion-classification']	['medical']	[ 5.73239625e-01 4.27833855e-01 3.78530234e-01 -1.80307880e-01 -5.87979615e-01 -8.08549225e-01 6.29976094e-01 -4.99434732e-02 -3.79427433e-01 5.33894598e-01 -2.82726973e-01 -3.29266608e-01 -3.11361756e-02 -4.24515456e-01 -7.50558734e-01 -7.56179512e-01 -2.20316425e-01 7.83680379e-02 2.13455319e-01 -8.81191343e-02 -1.24837548e-01 7.79139578e-01 -7.20400751e-01 4.47753698e-01 8.60881507e-01 8.48270297e-01 -5.53910315e-01 5.69209754e-01 4.12236422e-01 5.63199878e-01 -7.96374202e-01 -7.62747645e-01 3.36758792e-01 -1.57709703e-01 -8.92033517e-01 1.92095309e-01 9.59516764e-01 -3.02360207e-01 -7.04773903e-01 1.52847970e+00 5.74035585e-01 -2.34643981e-01 9.25355375e-01 -1.41766548e+00 -8.18332255e-01 4.80262667e-01 -2.54983902e-01 3.02537024e-01 1.18393816e-01 7.62678504e-01 3.55615973e-01 -5.44286728e-01 6.44221783e-01 1.15173471e+00 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e231fea6-49aa-4e27-95d5-33d583499180	brain-tumor-segmentation-from-mri-images	2305.00257	null	https://arxiv.org/abs/2305.00257v1	https://arxiv.org/pdf/2305.00257v1.pdf	Brain Tumor Segmentation from MRI Images using Deep Learning Techniques	A brain tumor, whether benign or malignant, can potentially be life threatening and requires painstaking efforts in order to identify the type, origin and location, let alone cure one. Manual segmentation by medical specialists can be time-consuming, which calls out for the involvement of technology to hasten the process with high accuracy. For the purpose of medical image segmentation, we inspected and identified the capable deep learning model, which shows consistent results in the dataset used for brain tumor segmentation. In this study, a public MRI imaging dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor. The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models like U-Net & Attention U-Net with various backbones, Deep Residual U-Net, ResUnet++ and Recurrent Residual U-Net. with varying parameters, acquired from our review of the literature related to human brain tumor classification and segmentation. The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam optimizer reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models. The visual findings also show the remarkable results of the brain tumor segmentation from MRI scans and demonstrates how useful the algorithm will be for physicians to extract the brain cancers automatically from MRI scans and serve humanity.	['Atul Dayal', 'Attulya Singh', 'Vipul Kumar Mishra', 'Mayank Dixit', 'Ayan Gupta']	2023-04-29	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation']	['computer-vision', 'medical']	[ 1.23751923e-01 4.64098573e-01 -1.09632639e-02 -1.87053159e-01 -6.10010386e-01 -1.42764479e-01 2.76741177e-01 3.87356542e-02 -7.09342778e-01 7.87462056e-01 -6.44829050e-02 -6.10207558e-01 -1.97701707e-01 -5.34470379e-01 -4.22814369e-01 -1.01907265e+00 -2.44426802e-01 9.10830140e-01 2.08441198e-01 -6.56628236e-02 3.47067535e-01 7.25009620e-01 -8.78829539e-01 1.87364649e-02 8.40293109e-01 1.16284156e+00 5.00273943e-01 6.46018922e-01 -4.21091527e-01 6.61469281e-01 -5.97885311e-01 -7.54463822e-02 2.89626688e-01 -1.44568697e-01 -1.05141973e+00 1.33913457e-01 -2.27140695e-01 -2.57991374e-01 -3.16034168e-01 8.86533022e-01 6.27754092e-01 -1.08284280e-01 9.59241688e-01 -9.67258632e-01 -3.60555470e-01 5.74655354e-01 -7.87537873e-01 7.06078410e-01 -1.12715550e-01 2.03388438e-01 2.93771386e-01 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245f969e-c910-420e-8543-56d02a628ec6	spact-self-supervised-privacy-preservation	2203.15205	null	https://arxiv.org/abs/2203.15205v1	https://arxiv.org/pdf/2203.15205v1.pdf	SPAct: Self-supervised Privacy Preservation for Action Recognition	Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels. Our training framework consists of three main components: anonymization function, self-supervised privacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and maximize the privacy cost function through a contrastive self-supervised loss. Employing existing protocols of known-action and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. In addition, we introduce a new protocol to evaluate the generalization of learned the anonymization function to novel-action and privacy attributes and show that our self-supervised framework outperforms existing supervised methods. Code available at: https://github.com/DAVEISHAN/SPAct	['Mubarak Shah', 'Chen Chen', 'Ishan Rajendrakumar Dave']	2022-03-29	null	http://openaccess.thecvf.com//content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.pdf	cvpr-2022-1	['action-classification']	['computer-vision']	[ 5.08843422e-01 1.94201797e-01 -4.92180735e-01 -7.73573577e-01 -1.03201747e+00 -8.60000610e-01 3.59985590e-01 1.74597055e-02 -5.73280334e-01 7.24887848e-01 3.52915287e-01 -3.66918072e-02 5.46734557e-02 -5.25148332e-01 -9.57171679e-01 -8.14303577e-01 4.81215753e-02 -1.01749405e-01 2.88687218e-02 5.23854196e-01 -2.15145689e-03 4.15655762e-01 -1.35329580e+00 4.95708048e-01 7.40518928e-01 1.13985932e+00 -5.52475870e-01 3.54543656e-01 2.14370847e-01 1.04004347e+00 -2.51250952e-01 -6.48655355e-01 8.37727070e-01 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7a9cc8a1-a2cd-4a45-b531-56e5bc3094e1	temporal-transformer-networks-joint-learning-1	1906.05947	null	https://arxiv.org/abs/1906.05947v1	https://arxiv.org/pdf/1906.05947v1.pdf	Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping	Many time-series classification problems involve developing metrics that are invariant to temporal misalignment. In human activity analysis, temporal misalignment arises due to various reasons including differing initial phase, sensor sampling rates, and elastic time-warps due to subject-specific biomechanics. Past work in this area has only looked at reducing intra-class variability by elastic temporal alignment. In this paper, we propose a hybrid model-based and data-driven approach to learn warping functions that not just reduce intra-class variability, but also increase inter-class separation. We call this a temporal transformer network (TTN). TTN is an interpretable differentiable module, which can be easily integrated at the front end of a classification network. The module is capable of reducing intra-class variance by generating input-dependent warping functions which lead to rate-robust representations. At the same time, it increases inter-class variance by learning warping functions that are more discriminative. We show improvements over strong baselines in 3D action recognition on challenging datasets using the proposed framework. The improvements are especially pronounced when training sets are smaller.	['Suhas Lohit', 'Pavan Turaga', 'Qiao Wang']	2019-06-13	temporal-transformer-networks-joint-learning	http://openaccess.thecvf.com/content_CVPR_2019/html/Lohit_Temporal_Transformer_Networks_Joint_Learning_of_Invariant_and_Discriminative_Time_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Lohit_Temporal_Transformer_Networks_Joint_Learning_of_Invariant_and_Discriminative_Time_CVPR_2019_paper.pdf	cvpr-2019-6	['3d-human-action-recognition']	['computer-vision']	[ 6.21501207e-01 -2.36428455e-01 -2.74416149e-01 -4.30586070e-01 -6.48190618e-01 -5.75344145e-01 6.99619174e-01 -1.14527248e-01 -3.91206115e-01 4.69469130e-01 4.98059064e-01 1.06587619e-01 -4.44135040e-01 -4.42029148e-01 -5.71094334e-01 -7.37866819e-01 -2.42164582e-01 1.59447312e-01 2.70395190e-01 -1.34466335e-01 1.65795311e-01 5.96464038e-01 -1.47216105e+00 3.20677698e-01 6.38277054e-01 1.04378164e+00 -1.91986188e-01 4.35142338e-01 2.81830877e-01 5.32293856e-01 -5.80858052e-01 1.89521149e-01 3.06729078e-01 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010cf2d6-3e16-43a9-bf08-afee3f10ed07	deep-neural-networks-for-visual-reasoning	2209.11990	null	https://arxiv.org/abs/2209.11990v1	https://arxiv.org/pdf/2209.11990v1.pdf	Deep Neural Networks for Visual Reasoning	Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions. It is crucial for machines to have this capacity to reason using these two modalities to invent new robot-human collaborative systems. Recent advances in deep learning have built separate sophisticated representations of both visual scenes and languages. However, understanding the associations between the two modalities in a shared context for multimodal reasoning remains a challenge. Focusing on language and vision modalities, this thesis advances the understanding of how to exploit and use pivotal aspects of vision-and-language tasks with neural networks to support reasoning. We derive these understandings from a series of works, making a two-fold contribution: (i) effective mechanisms for content selection and construction of temporal relations from dynamic visual scenes in response to a linguistic query and preparing adequate knowledge for the reasoning process (ii) new frameworks to perform reasoning with neural networks by exploiting visual-linguistic associations, deduced either directly from data or guided by external priors.	['Thao Minh Le']	2022-09-24	null	null	null	null	['visual-reasoning', 'visual-reasoning']	['computer-vision', 'reasoning']	[ 6.03455752e-02 1.60966456e-01 -1.72417816e-02 -6.38900399e-01 -7.42652118e-02 -6.72178030e-01 1.09423590e+00 2.03022003e-01 -4.44480509e-01 4.58805621e-01 3.73545289e-01 -3.26843351e-01 -2.03286320e-01 -6.77009881e-01 -4.78265047e-01 -3.66929233e-01 -5.07962815e-02 5.13713181e-01 1.83626026e-01 -4.64282393e-01 3.91514659e-01 9.26153362e-01 -1.87208176e+00 9.97066259e-01 2.24912092e-01 7.96126246e-01 6.44528985e-01 9.45431948e-01 -5.80009401e-01 1.60352993e+00 -1.76152349e-01 -2.43988901e-01 -5.47639094e-02 -4.81021374e-01 -1.26689756e+00 9.08465683e-02 4.06915769e-02 -1.34857714e-01 -2.28043690e-01 7.84245014e-01 1.13935396e-01 3.18837732e-01 7.57323444e-01 -1.20069897e+00 -8.99437249e-01 3.41058135e-01 -1.76910535e-01 1.45785093e-01 7.92528212e-01 2.71098018e-01 8.79523873e-01 -7.09958673e-01 7.96424270e-01 1.62002885e+00 2.59971887e-01 5.55498183e-01 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-2.20633179e-01 5.17867565e-01 1.77179813e-01 -5.97054839e-01 -1.08652666e-01 5.96269310e-01]	[10.609118461608887, 1.8569799661636353]
0e877f83-5a83-46ce-aee1-a615e391e614	intelligent-trading-systems-a-sentiment-aware	2112.02095	null	https://arxiv.org/abs/2112.02095v1	https://arxiv.org/pdf/2112.02095v1.pdf	Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach	The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. Moreover, some issues are still not addressed, such as extracting market sentiment momentum through the explicit capture of sentiment features that reflect the market condition over time and assessing the consistency and stability of RL results in different situations. Filling this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading system that improves profit stability by leveraging market mood through an adaptive amount of past sentiment features drawn from textual news. We evaluated SentARL across twenty assets, two transaction costs, and five different periods and initializations to show its consistent effectiveness against baselines. Subsequently, this thorough assessment allowed us to identify the boundary between news coverage and market sentiment regarding the correlation of price-time series above which SentARL's effectiveness is outstanding.	['Anna Helena Reali Costa', 'Reinaldo Augusto da Costa Bianchi', 'Leonardo Kanashiro Felizardo', 'Francisco Caio Lima Paiva']	2021-11-14	null	null	null	null	['algorithmic-trading']	['time-series']	[-3.50806177e-01 -1.48015723e-01 -6.26353741e-01 -3.94353062e-01 -7.21987665e-01 -9.53579783e-01 9.10979092e-01 3.44859213e-01 -3.64361823e-01 6.94879889e-01 3.16680938e-01 -3.47968936e-01 -2.31564298e-01 -9.90277231e-01 -3.55098516e-01 -3.37548494e-01 -3.38171989e-01 3.01600128e-01 -1.06776707e-01 -6.43109560e-01 6.74574614e-01 1.78532869e-01 -1.32621932e+00 3.58159512e-01 6.05275869e-01 1.53459728e+00 -2.71203727e-01 1.18993111e-01 -3.51962537e-01 1.18697166e+00 -7.09452689e-01 -5.70064664e-01 7.10629344e-01 -4.09076720e-01 -4.51047063e-01 1.73174106e-02 -2.05382466e-01 -1.90102756e-01 3.23875487e-01 9.08850908e-01 1.62616268e-01 -2.33338952e-01 3.56325686e-01 -1.01376534e+00 -5.71388960e-01 1.15618360e+00 -5.37586331e-01 6.57828331e-01 1.92788243e-01 1.89540625e-01 1.78889751e+00 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646844e4-58a3-4681-a120-e4cd4f244b7e	self-supervised-viewpoint-learning-from-image	2004.01793	null	https://arxiv.org/abs/2004.01793v1	https://arxiv.org/pdf/2004.01793v1.pdf	Self-Supervised Viewpoint Learning From Image Collections	Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such unlabeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV.	['Shalini De Mello', 'Sifei Liu', 'Jan Kautz', 'Umar Iqbal', 'Carsten Rother', 'Varun Jampani', 'Siva Karthik Mustikovela']	2020-04-03	self-supervised-viewpoint-learning-from-image-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.pdf	cvpr-2020-6	['viewpoint-estimation']	['computer-vision']	[ 2.23080739e-01 3.99872124e-01 2.99376566e-02 -8.52850318e-01 -7.18627810e-01 -8.06068838e-01 4.93938148e-01 -6.00671291e-01 -7.19227642e-02 4.86485273e-01 -2.67174840e-01 -1.31251723e-01 3.96679223e-01 -7.53280699e-01 -1.16503239e+00 -7.80385673e-01 4.98050570e-01 6.88117385e-01 1.40341595e-01 -1.21150158e-01 3.88071537e-02 5.67433000e-01 -1.64424586e+00 2.06918463e-01 3.42230260e-01 1.02878499e+00 1.47673979e-01 3.83711070e-01 3.74239385e-01 9.44541156e-01 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8d80a585-882d-49e2-ab35-7c2e4b2809a4	in-search-of-a-robust-facial-expressions	null	null	https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656	https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656	In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study	Many researchers have been seeking robust emotion recognition system for already last two decades. It would advance computer systems to a new level of interaction, providing much more natural feedback during human–computer interaction due to analysis of user affect state. However, one of the key problems in this domain is a lack of generalization ability: we observe dramatic degradation of model performance when it was trained on one corpus and evaluated on another one. Although some studies were done in this direction, visual modality still remains under-investigated. Therefore, we introduce the visual cross-corpus study conducted with the utilization of eight corpora, which differ in recording conditions, participants’ appearance characteristics, and complexity of data processing. We propose a visual-based end-to-end emotion recognition framework, which consists of the robust pre-trained backbone model and temporal sub-system in order to model temporal dependencies across many video frames. In addition, a detailed analysis of mistakes and advantages of the backbone model is provided, demonstrating its high ability of generalization. Our results show that the backbone model has achieved the accuracy of 66.4% on the AffectNet dataset, outperforming all the state-of-the-art results. Moreover, the CNN-LSTM model has demonstrated a decent efficacy on dynamic visual datasets during cross-corpus experiments, achieving comparable with state-of-the-art results. In addition, we provide backbone and CNN-LSTM models for future researchers: they can be accessed via GitHub.	['Alexey Karpov', 'Denis Dresvyanskiy', 'Elena Ryumina']	2022-10-07	null	null	null	neurocomputing-2022-10	['face-detection', 'facial-expression-recognition', 'cross-corpus']	['computer-vision', 'computer-vision', 'computer-vision']	[-3.29752006e-02 -4.88651007e-01 8.66580680e-02 -4.18518692e-01 -2.87597865e-01 -3.38623762e-01 4.48769361e-01 3.77753153e-02 -5.48893094e-01 3.08015823e-01 9.33354944e-02 1.21631324e-01 4.13548380e-01 -1.00956544e-01 -3.71891379e-01 -4.55955893e-01 -4.02579814e-01 -1.95999518e-01 -5.84927127e-02 -2.23948270e-01 -8.39234591e-02 3.25984716e-01 -1.68677235e+00 7.02666342e-01 5.42859733e-01 1.29323351e+00 -2.90291104e-02 6.19360626e-01 1.17676511e-01 7.36231387e-01 -6.53303921e-01 -5.30791223e-01 -1.90309957e-01 -3.66617352e-01 -6.30180061e-01 4.60844208e-03 3.42061311e-01 -2.67746061e-01 -2.74530053e-01 7.87477314e-01 8.93768013e-01 2.83687025e-01 4.02965873e-01 -1.29031193e+00 -7.27889955e-01 2.41732262e-02 -5.93489051e-01 2.06189990e-01 5.25789618e-01 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e5eea6f6-99f6-4ffc-9ead-161c4599594d	modular-transformers-compressing-transformers	2306.02379	null	https://arxiv.org/abs/2306.02379v1	https://arxiv.org/pdf/2306.02379v1.pdf	Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference	Pre-trained Transformer models like T5 and BART have advanced the state of the art on a wide range of text generation tasks. Compressing these models into smaller ones has become critically important for practical use. Common neural network compression techniques such as knowledge distillation or quantization are limited to static compression where the compression ratio is fixed. In this paper, we introduce Modular Transformers, a modularized encoder-decoder framework for flexible sequence-to-sequence model compression. Modular Transformers train modularized layers that have the same function of two or more consecutive layers in the original model via module replacing and knowledge distillation. After training, the modularized layers can be flexibly assembled into sequence-to-sequence models that meet different performance-efficiency trade-offs. Experimental results show that after a single training phase, by simply varying the assembling strategy, Modular Transformers can achieve flexible compression ratios from 1.1x to 6x with little to moderate relative performance drop.	['Yejin Choi', 'Ronan Le Bras', 'Wangchunshu Zhou']	2023-06-04	null	null	null	null	['neural-network-compression', 'quantization', 'model-compression', 'neural-network-compression']	['methodology', 'methodology', 'methodology', 'miscellaneous']	[ 5.65631986e-01 3.16662014e-01 -3.90105814e-01 -1.96401820e-01 -6.48712456e-01 -4.89234090e-01 5.51232517e-01 1.54347017e-01 -4.70523179e-01 7.93517113e-01 1.52743429e-01 -5.27182341e-01 1.04575947e-01 -7.15008318e-01 -9.34911788e-01 -4.06005472e-01 1.98769286e-01 6.04171336e-01 1.83891997e-01 -1.36977047e-01 4.28330600e-02 1.24559611e-01 -1.42403507e+00 6.47567332e-01 9.61465478e-01 8.99231791e-01 6.90502048e-01 9.64527786e-01 -2.44711414e-01 7.18919158e-01 -7.01403975e-01 -7.72032380e-01 2.69575953e-01 -4.92475033e-01 -7.91383088e-01 -3.10332417e-01 1.34057462e-01 -5.85406065e-01 -6.53787315e-01 8.18244755e-01 5.13158381e-01 -2.58357286e-01 5.39495230e-01 -8.95208895e-01 -3.92602473e-01 1.37640631e+00 -3.09504271e-01 -6.85657337e-02 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18c8019c-b3bf-4767-a1f0-c7a745900581	sentence-boundary-detection-on-line-breaks-in	null	null	https://aclanthology.org/2020.wnut-1.10	https://aclanthology.org/2020.wnut-1.10.pdf	Sentence Boundary Detection on Line Breaks in Japanese	For NLP, sentence boundary detection (SBD) is an essential task to decompose a text into sentences. Most of the previous studies have used a simple rule that uses only typical characters as sentence boundaries. However, some characters may or may not be sentence boundaries depending on the context. We focused on line breaks in them. We newly constructed annotated corpora, implemented sentence boundary detectors, and analyzed performance of SBD in several settings.	['Kensuke Mitsuzawa', 'Yuta Hayashibe']	null	null	null	null	emnlp-wnut-2020-11	['boundary-detection']	['computer-vision']	[ 1.60449460e-01 -3.77564393e-02 -1.34388223e-01 -4.86846089e-01 -5.76509476e-01 -8.14310312e-01 2.29373679e-01 6.62194312e-01 -3.31441194e-01 9.39201295e-01 1.92093849e-01 -6.77924335e-01 4.08402801e-01 -6.44417703e-01 -2.96501458e-01 4.51604389e-02 7.41410404e-02 1.19116634e-01 9.60768700e-01 -2.35164225e-01 5.37728369e-01 2.16354027e-01 -7.80246556e-01 4.84454453e-01 1.15367615e+00 4.75323021e-01 2.67416447e-01 8.81535709e-01 -9.22468901e-01 2.05161631e-01 -1.39136219e+00 -1.11997716e-01 6.14442863e-03 -5.79529822e-01 -9.10500586e-01 -3.66373621e-02 2.06639230e-01 6.50347546e-02 -7.55083486e-02 1.28551590e+00 2.65138865e-01 -1.53993433e-02 5.12549222e-01 -9.50858891e-01 -5.18054485e-01 9.73546803e-01 -6.00666821e-01 7.30135322e-01 9.53156710e-01 -2.64066994e-01 9.18356001e-01 -7.41929471e-01 8.04925382e-01 1.01158834e+00 6.94769800e-01 7.83044100e-01 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ed287a0f-1b53-46c1-8375-dcf71b5f92b4	generating-2d-and-3d-master-faces-for	2211.13964	null	https://arxiv.org/abs/2211.13964v2	https://arxiv.org/pdf/2211.13964v2.pdf	Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution	A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. For 2D face verification, multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network to direct the search toward promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network. When employing two different 3D face recognition systems, we are able to obtain a coverage of 40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master faces, which simultaneously match 2D and 3D models with high impersonation rates.	['Lior Wolf', 'Ron Shmelkin', 'Tomer Friedlander']	2022-11-25	null	null	null	null	['3d-face-reconstruction', 'face-reconstruction']	['computer-vision', 'computer-vision']	[ 3.39271873e-01 2.97141165e-01 2.59115607e-01 -3.03579688e-01 -4.29597676e-01 -8.59852254e-01 6.66315734e-01 -8.19473207e-01 -8.78092274e-02 5.33590913e-01 -3.69116485e-01 -3.26659590e-01 1.22316338e-01 -8.73338878e-01 -5.95657110e-01 -8.07420909e-01 -1.65395647e-01 5.91297626e-01 -6.21863246e-01 -3.04230034e-01 1.01668619e-01 1.08963895e+00 -2.07580304e+00 2.17494428e-01 3.03123593e-01 1.10397613e+00 -5.76113462e-01 9.52358067e-01 2.69163191e-01 4.88744974e-02 -9.48078215e-01 -9.80674803e-01 7.85442173e-01 -6.84965670e-01 -4.68049586e-01 -1.74119532e-01 6.57815397e-01 -3.48493814e-01 -1.05792195e-01 9.28497970e-01 1.03756988e+00 -5.57445884e-01 6.55793369e-01 -1.53337038e+00 -6.57299221e-01 2.18053773e-01 -3.76770824e-01 -5.22791684e-01 8.68455887e-01 4.97622937e-01 3.66959304e-01 -9.57692862e-01 7.10793376e-01 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1.38232219e+00 -1.12902749e+00 -3.79227042e-01 -4.00954902e-01 -4.34222519e-01 -2.88053658e-02 4.74150002e-01 -4.57015663e-01 -4.87533003e-01 -1.62721816e-02]	[12.808758735656738, 0.7714151740074158]
da8176c7-d0f3-418c-9e3f-f87489d94a4c	the-emergence-of-objectness-learning-zero	2111.06394	null	https://arxiv.org/abs/2111.06394v1	https://arxiv.org/pdf/2111.06394v1.pdf	The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos	Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement concurrently from unlabeled videos. Our premise is that a video has different views of the same scene related by moving components, and the right region segmentation and region flow would allow mutual view synthesis which can be checked from the data itself without any external supervision. Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images. It then binds them in a conjoint representation called segment flow that pools flow offsets over each region and provides a gross characterization of moving regions for the entire scene. By training the model to minimize view synthesis errors based on segment flow, our appearance and motion pathways learn region segmentation and flow estimation automatically without building them up from low-level edges or optical flows respectively. Our model demonstrates the surprising emergence of objectness in the appearance pathway, surpassing prior works on zero-shot object segmentation from an image, moving object segmentation from a video with unsupervised test-time adaptation, and semantic image segmentation by supervised fine-tuning. Our work is the first truly end-to-end zero-shot object segmentation from videos. It not only develops generic objectness for segmentation and tracking, but also outperforms prevalent image-based contrastive learning methods without augmentation engineering.	['Stephen Lin', 'Stella X. Yu', 'Zhirong Wu', 'Runtao Liu']	2021-11-11	null	http://proceedings.neurips.cc/paper/2021/hash/6d9cb7de5e8ac30bd5e8734bc96a35c1-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/6d9cb7de5e8ac30bd5e8734bc96a35c1-Paper.pdf	neurips-2021-12	['zero-shot-segmentation', 'unsupervised-object-segmentation', 'video-polyp-segmentation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 3.78126502e-01 1.80577382e-01 -5.20677686e-01 -3.69334459e-01 -4.48754787e-01 -9.36446905e-01 4.47493225e-01 -4.12975669e-01 -1.01482362e-01 3.40413749e-01 8.94069299e-03 -5.59445620e-02 2.18567982e-01 -5.95721185e-01 -9.51551437e-01 -5.34560740e-01 1.25403553e-02 4.54323024e-01 7.55821109e-01 -2.34530699e-02 2.62274265e-01 3.90485346e-01 -1.41579580e+00 4.04616952e-01 7.06160009e-01 8.94435346e-01 3.17151427e-01 1.19538069e+00 -1.83550343e-01 1.11953211e+00 -1.04391046e-01 -1.14138365e-01 6.18055582e-01 -7.31253386e-01 -1.27986014e+00 8.16480219e-01 1.05643845e+00 -8.00805271e-01 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1e1bb31d-3b3b-4235-9d7f-ebaa85c22d15	a-probabilistic-graphical-model-based-on	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Yu_A_Probabilistic_Graphical_Model_Based_on_Neural-Symbolic_Reasoning_for_Visual_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_A_Probabilistic_Graphical_Model_Based_on_Neural-Symbolic_Reasoning_for_Visual_CVPR_2022_paper.pdf	A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection	This paper aims to leverage symbolic knowledge to improve the performance and interpretability of the Visual Relationship Detection (VRD) models. Existing VRD methods based on deep learning suffer from the problems of poor performance on insufficient labeled examples and lack of interpretability. To overcome the aforementioned weaknesses, we integrate symbolic knowledge into deep learning models and propose a bi-level probabilistic graphical reasoning framework called BPGR. Specifically, in the high-level structure, we take the objects and relationships detected by the VRD model as hidden variables (reasoning results); In the low-level structure of BPGR, we use Markov Logic Networks (MLNs) to project First-Order Logic (FOL) as observed variables (symbolic knowledge) to correct error reasoning results. We adopt a variational EM algorithm for optimization. Experiments results show that our BPGR improves the performance of the VRD models. In particular, BPGR can also provide easy-to-understand insights for reasoning results to show interpretability.	['Shirui Pan', 'Anchen Li', 'Qianhao Wei', 'Bo Yang', 'Dongran Yu']	2022-01-01	null	null	null	cvpr-2022-1	['visual-relationship-detection']	['computer-vision']	[-2.63211370e-01 6.39504433e-01 -4.81940806e-01 -3.53525609e-01 -3.39545310e-01 -2.12850422e-01 5.54849088e-01 -9.40327495e-02 3.34391028e-01 5.08967578e-01 5.04682623e-02 -7.62854576e-01 -4.40313786e-01 -1.07410717e+00 -1.02044630e+00 -2.87034661e-01 4.95100878e-02 6.79680705e-01 -1.19037153e-02 1.73547581e-01 -2.04307482e-01 3.79021645e-01 -1.32935929e+00 5.67112386e-01 1.05174088e+00 8.03320348e-01 -2.00307593e-02 2.62633234e-01 -3.85963172e-01 1.57093048e+00 -2.90267140e-01 -6.01396024e-01 -1.98583707e-01 -1.87057093e-01 -9.07463551e-01 -2.84026176e-01 -4.58293334e-02 -4.27422374e-01 -3.77338380e-01 1.22673166e+00 -2.45336950e-01 1.04275838e-01 8.68771374e-01 -1.81493449e+00 -1.34202588e+00 1.10567820e+00 -6.16691232e-01 -2.70684183e-01 4.70794529e-01 8.26274306e-02 1.46680915e+00 -8.40331256e-01 6.87804639e-01 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e3885754-34b5-4141-bf2d-8507e8bc597c	how-do-you-do-it-fine-grained-action	2203.12344	null	https://arxiv.org/abs/2203.12344v2	https://arxiv.org/pdf/2203.12344v2.pdf	How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs	We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a method which recognizes adverbs across different actions. However, such fine-grained annotations are difficult to obtain and their long-tailed nature makes it challenging to recognize adverbs in rare action-adverb compositions. Our approach therefore uses semi-supervised learning with multiple adverb pseudo-labels to leverage videos with only action labels. Combined with adaptive thresholding of these pseudo-adverbs we are able to make efficient use of the available data while tackling the long-tailed distribution. Additionally, we gather adverb annotations for three existing video retrieval datasets, which allows us to introduce the new tasks of recognizing adverbs in unseen action-adverb compositions and unseen domains. Experiments demonstrate the effectiveness of our method, which outperforms prior work in recognizing adverbs and semi-supervised works adapted for adverb recognition. We also show how adverbs can relate fine-grained actions.	['Cees G. M. Snoek', 'Hazel Doughty']	2022-03-23	null	http://openaccess.thecvf.com//content/CVPR2022/html/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.pdf	cvpr-2022-1	['action-understanding']	['computer-vision']	[ 3.94550800e-01 -2.85735339e-01 -5.89146137e-01 -5.36650121e-01 -9.99534547e-01 -9.95629251e-01 6.40354514e-01 9.18802395e-02 -2.73175925e-01 5.24170160e-01 4.96469259e-01 1.13331988e-01 -7.13161305e-02 -4.32439774e-01 -1.14005411e+00 -7.45004833e-01 -4.28013019e-02 4.24605727e-01 6.84047818e-01 -3.36760163e-01 1.82013243e-01 2.72955447e-01 -1.90065801e+00 1.01194811e+00 2.67553598e-01 1.28788996e+00 -1.07855067e-01 6.38131440e-01 2.87054293e-02 1.09152198e+00 -5.96710324e-01 -4.71121550e-01 3.00839275e-01 -3.66710335e-01 -8.63046706e-01 5.98505437e-01 1.14969468e+00 -5.26320338e-01 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39a287ad-814c-419d-a9d3-80c194d48a32	audio-visual-deception-detection-dolos	2303.12745	null	https://arxiv.org/abs/2303.12745v1	https://arxiv.org/pdf/2303.12745v1.pdf	Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning	Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this issue, we introduce DOLOS, the largest gameshow deception detection dataset with rich deceptive conversations. DOLOS includes 1,675 video clips featuring 213 subjects, and it has been labeled with audio-visual feature annotations. We provide train-test, duration, and gender protocols to investigate the impact of different factors. We benchmark our dataset on previously proposed deception detection approaches. To further improve the performance by fine-tuning fewer parameters, we propose Parameter-Efficient Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter) explores temporal attention in transformer-based architectures, and a crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines crossmodal information from audio-visual features. Based on the rich fine-grained audio-visual annotations on DOLOS, we also exploit multi-task learning to enhance performance by concurrently predicting deception and audio-visual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes will be publicly available soon.	['Alex Kot', 'Bingquan Shen', 'Adams Kong', 'Zitong Yu', 'Nithish Muthuchamy Selvaraj', 'Xiaobao Guo']	2023-03-09	null	null	null	null	['deception-detection']	['miscellaneous']	[-1.17800675e-01 -4.67836261e-01 -9.83635858e-02 -3.97974104e-01 -1.23926842e+00 -6.38755262e-01 7.63794661e-01 -9.71117392e-02 -2.57295281e-01 4.94441926e-01 4.65676725e-01 -1.61003396e-01 -7.46833608e-02 -1.16591334e-01 -4.65255171e-01 -5.85717559e-01 5.67979366e-02 -6.05958654e-03 1.15581222e-01 -2.60319710e-01 4.07278597e-01 3.38420212e-01 -1.49469495e+00 7.65300512e-01 9.02875185e-01 1.39763212e+00 -5.96897185e-01 6.82830036e-01 4.61207509e-01 8.50429416e-01 -8.36548567e-01 -1.21155703e+00 -7.87547007e-02 -3.59477341e-01 -5.26416779e-01 -7.26561819e-04 4.56073403e-01 -6.07078314e-01 -5.26371956e-01 8.28453422e-01 6.80974901e-01 1.30227670e-01 5.91165960e-01 -1.87943113e+00 -5.64506531e-01 2.53868222e-01 -6.26626313e-01 3.75038296e-01 5.85806310e-01 3.24708760e-01 1.07469428e+00 -9.90135729e-01 8.73756558e-02 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8e88663f-0df6-4fd2-8dd3-a1a7763f2a17	structured-attention-composition-for-temporal	2205.09956	null	https://arxiv.org/abs/2205.09956v2	https://arxiv.org/pdf/2205.09956v2.pdf	Structured Attention Composition for Temporal Action Localization	Temporal action localization aims at localizing action instances from untrimmed videos. Existing works have designed various effective modules to precisely localize action instances based on appearance and motion features. However, by treating these two kinds of features with equal importance, previous works cannot take full advantage of each modality feature, making the learned model still sub-optimal. To tackle this issue, we make an early effort to study temporal action localization from the perspective of multi-modality feature learning, based on the observation that different actions exhibit specific preferences to appearance or motion modality. Specifically, we build a novel structured attention composition module. Unlike conventional attention, the proposed module would not infer frame attention and modality attention independently. Instead, by casting the relationship between the modality attention and the frame attention as an attention assignment process, the structured attention composition module learns to encode the frame-modality structure and uses it to regularize the inferred frame attention and modality attention, respectively, upon the optimal transport theory. The final frame-modality attention is obtained by the composition of the two individual attentions. The proposed structured attention composition module can be deployed as a plug-and-play module into existing action localization frameworks. Extensive experiments on two widely used benchmarks show that the proposed structured attention composition consistently improves four state-of-the-art temporal action localization methods and builds new state-of-the-art performance on THUMOS14. Code is availabel at https://github.com/VividLe/Structured-Attention-Composition.	['Dingwen Zhang', 'Nian Liu', 'Tao Zhao', 'Junwei Han', 'Le Yang']	2022-05-20	null	null	null	null	['action-localization']	['computer-vision']	[ 2.72376776e-01 -5.01690209e-02 -5.26300907e-01 -1.42990068e-01 -8.89065921e-01 -3.00321192e-01 7.31206894e-01 -1.18484095e-01 -4.07247841e-01 5.21044791e-01 5.22327721e-01 2.85487417e-02 -5.08570559e-02 -3.24018866e-01 -8.35752249e-01 -8.86355758e-01 1.37476042e-01 2.14993432e-01 4.51332688e-01 2.09758226e-02 2.62184918e-01 7.48747811e-02 -1.30763590e+00 4.24690098e-01 6.47611439e-01 1.01417577e+00 2.99198717e-01 5.04323304e-01 -3.62057090e-02 1.27226961e+00 -6.43289909e-02 -1.10900663e-01 1.87580764e-01 -7.49702692e-01 -1.07200539e+00 2.87921578e-01 3.02268893e-01 -4.80486721e-01 -3.13242972e-01 8.26399326e-01 3.37617397e-01 4.52831984e-01 3.55853945e-01 -1.42282450e+00 -8.07527959e-01 4.80979830e-01 -7.86883414e-01 5.10771394e-01 5.20624757e-01 2.96599954e-01 1.23355126e+00 -7.85685718e-01 4.08423245e-01 1.12273109e+00 3.71789873e-01 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1ae7821a-f352-447b-ac62-2b76545b65bd	on-the-evaluation-of-user-privacy-in-deep	2208.01113	null	https://arxiv.org/abs/2208.01113v2	https://arxiv.org/pdf/2208.01113v2.pdf	On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel	Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an overly-exposed threat surface for privacy violations. Furthermore, the widespread use of cloud-based Machine-Learning-as-a-Service (MLaaS) for its robust infrastructure support has broadened the threat surface to include a variety of remote side-channel attacks. In this paper, we first identify and report a novel data-dependent timing side-channel leakage (termed Class Leakage) in DL implementations originating from non-constant time branching operation in a widely used DL framework PyTorch. We further demonstrate a practical inference-time attack where an adversary with user privilege and hard-label black-box access to an MLaaS can exploit Class Leakage to compromise the privacy of MLaaS users. DL models are vulnerable to Membership Inference Attack (MIA), where an adversary's objective is to deduce whether any particular data has been used while training the model. In this paper, as a separate case study, we demonstrate that a DL model secured with differential privacy (a popular countermeasure against MIA) is still vulnerable to MIA against an adversary exploiting Class Leakage. We develop an easy-to-implement countermeasure by making a constant-time branching operation that alleviates the Class Leakage and also aids in mitigating MIA. We have chosen two standard benchmarking image classification datasets, CIFAR-10 and CIFAR-100 to train five state-of-the-art pre-trained DL models, over two different computing environments having Intel Xeon and Intel i7 processors to validate our approach.	['Pabitra Mitra', 'Debdeep Mukhopadhyay', 'Sarani Bhattacharya', 'Manaar Alam', 'Shubhi Shukla']	2022-08-01	null	null	null	null	['inference-attack', 'membership-inference-attack']	['adversarial', 'computer-vision']	[ 1.99060902e-01 -2.01749071e-01 -5.72944432e-02 -6.63642585e-01 -1.04268157e+00 -1.16293025e+00 5.44170499e-01 1.54561833e-01 -6.92740440e-01 6.87702477e-01 -4.52063829e-01 -1.16949415e+00 1.33079916e-01 -8.74981284e-01 -1.11079574e+00 -9.35852706e-01 -3.83099020e-01 1.01413019e-02 -4.15971875e-02 1.97104424e-01 3.81607451e-02 9.45659637e-01 -1.16294205e+00 4.87297118e-01 3.18361521e-01 1.02067792e+00 -6.39897108e-01 6.48561060e-01 3.94925177e-01 5.55209875e-01 -9.17812884e-01 -6.26649201e-01 8.92039180e-01 8.50122720e-02 -8.33348930e-01 -4.58603203e-01 5.51047862e-01 -5.28204322e-01 -2.43435755e-01 1.06038582e+00 5.85613906e-01 -4.85435247e-01 1.49794891e-01 -1.89129150e+00 2.13473337e-03 4.46088970e-01 -4.92248654e-01 -1.79530133e-03 -7.81800970e-02 3.19459736e-01 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548bb502-1dbd-4219-aa25-92529a2bee73	openhands-making-sign-language-recognition	2110.05877	null	https://arxiv.org/abs/2110.05877v1	https://arxiv.org/pdf/2110.05877v1.pdf	OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages	AI technologies for Natural Languages have made tremendous progress recently. However, commensurate progress has not been made on Sign Languages, in particular, in recognizing signs as individual words or as complete sentences. We introduce OpenHands, a library where we take four key ideas from the NLP community for low-resource languages and apply them to sign languages for word-level recognition. First, we propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference, and we release standardized pose datasets for 6 different sign languages - American, Argentinian, Chinese, Greek, Indian, and Turkish. Second, we train and release checkpoints of 4 pose-based isolated sign language recognition models across all 6 languages, providing baselines and ready checkpoints for deployment. Third, to address the lack of labelled data, we propose self-supervised pretraining on unlabelled data. We curate and release the largest pose-based pretraining dataset on Indian Sign Language (Indian-SL). Fourth, we compare different pretraining strategies and for the first time establish that pretraining is effective for sign language recognition by demonstrating (a) improved fine-tuning performance especially in low-resource settings, and (b) high crosslingual transfer from Indian-SL to few other sign languages. We open-source all models and datasets in OpenHands with a hope that it makes research in sign languages more accessible, available here at https://github.com/AI4Bharat/OpenHands .	['Mitesh Khapra', 'Pratyush Kumar', 'Gokul NC', 'Prem Selvaraj']	2021-10-12	null	https://aclanthology.org/2022.acl-long.150	https://aclanthology.org/2022.acl-long.150.pdf	acl-2022-5	['sign-language-recognition']	['computer-vision']	[ 1.36528820e-01 -2.13772044e-01 -4.71938014e-01 -5.23601830e-01 -1.27827001e+00 -8.32514584e-01 4.75212187e-01 -7.48814166e-01 -7.03475177e-01 5.85383058e-01 7.72812545e-01 -2.48052612e-01 1.28093883e-01 -7.58737698e-02 -6.09638810e-01 -3.84127587e-01 2.05666982e-02 7.21473932e-01 8.98018703e-02 -6.81274906e-02 -1.26015723e-01 5.28408587e-01 -1.47890425e+00 3.87836069e-01 8.72491241e-01 4.22253639e-01 -7.91252926e-02 7.11106122e-01 6.01236150e-02 7.21202314e-01 -3.42854649e-01 -3.23447317e-01 3.70253652e-01 -5.45275450e-01 -9.61666048e-01 -1.27565637e-01 1.17385495e+00 -6.67856753e-01 -3.48790735e-01 4.86241639e-01 7.78505683e-01 -2.66886801e-01 6.00023389e-01 -1.07003593e+00 -7.09682465e-01 6.63062811e-01 -2.85646349e-01 -2.41748318e-01 2.43055329e-01 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81fd7d7d-e343-4046-bf26-2556a95fd103	contact-area-detector-using-cross-view	2008.07712	null	https://arxiv.org/abs/2008.07712v1	https://arxiv.org/pdf/2008.07712v1.pdf	Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects	The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly. However, this solution would not meet the accuracy requirement in applications due to projection error. Another standard approach is to train a neural network to infer touch actions from the collected visual data. This strategy would require significant amounts of training data to generalize over scale and viewpoint variations. A different approach to this problem is to identify whether a person has touched a defined object. In this work, we show that the solution to this problem can be straightforward. Specifically, we show that the contact between an object and a static surface can be identified by projecting the object onto the static surface through two different viewpoints and analyzing their 2D intersection. The object contacts the surface when the projected points are close to each other; we call this cross view projection consistency. Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement. For planar space, this mapping is the Homography transformation. This simple method can be easily adapted to real-life applications. In this paper, we apply our method to do office occupancy detection for studying the COVID-19 transmission pattern from an office desk in a meeting room using the contact information.	['Jacky Bibliowicz', 'Wilfredo Torres Calderon', 'Liviu Calin', 'Pan Zhang', 'Michael Lee', 'Alex Tessier', 'Bokyung Lee']	2020-08-18	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[ 4.20553565e-01 -4.00902748e-01 4.78424169e-02 -2.12519303e-01 5.42121567e-03 -5.65171957e-01 4.79384512e-01 -2.77314603e-01 -2.80333519e-01 3.04806858e-01 -3.20055634e-01 -8.14059004e-02 2.30552375e-01 -8.41040552e-01 -6.30441606e-01 -5.03443718e-01 3.23549479e-01 8.54911149e-01 2.06134692e-01 -1.04462832e-01 7.60915950e-02 6.90321863e-01 -1.19701266e+00 1.73953533e-01 2.20030591e-01 6.77035391e-01 1.63143039e-01 7.55388916e-01 -3.10109686e-02 -9.43908021e-02 -5.72261870e-01 -1.43872261e-01 5.65245092e-01 -2.62793124e-01 -4.25676048e-01 2.68081844e-01 5.26660681e-01 -7.40994811e-01 1.18166253e-01 9.84353244e-01 8.81154984e-02 -2.82096088e-01 7.71832228e-01 -1.35264421e+00 -1.46056846e-01 -5.41521132e-01 -8.51366043e-01 -1.43095791e-01 7.71105886e-01 -2.32768491e-01 4.17029053e-01 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41ce1259-dbeb-4f25-be8b-a5637e8bccde	conerf-controllable-neural-radiance-fields	2112.01983	null	https://arxiv.org/abs/2112.01983v2	https://arxiv.org/pdf/2112.01983v2.pdf	CoNeRF: Controllable Neural Radiance Fields	We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attributes as latent variables that are regressed by the neural network given the scene encoding. This leads to a few-shot learning framework, where attributes are discovered automatically by the framework, when annotations are not provided. We apply our method to various scenes with different types of controllable attributes (e.g. expression control on human faces, or state control in movement of inanimate objects). Overall, we demonstrate, to the best of our knowledge, for the first time novel view and novel attribute re-rendering of scenes from a single video.	['Andrea Tagliasacchi', 'Tomasz Trzciński', 'Marek Kowalski', 'Kwang Moo Yi', 'Kacper Kania']	2021-12-03	null	http://openaccess.thecvf.com//content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.pdf	cvpr-2022-1	['3d-face-modeling']	['computer-vision']	[ 4.89646137e-01 3.50145161e-01 -4.96028662e-02 -7.38064528e-01 -1.38724983e-01 -8.13745856e-01 6.79808497e-01 -1.99239209e-01 -2.47488543e-01 5.13069510e-01 1.66584358e-01 2.23860994e-01 9.56931114e-02 -6.07708991e-01 -9.25966322e-01 -5.36421776e-01 5.97466454e-02 4.59533215e-01 -1.15734212e-01 -5.54854870e-02 -1.05764754e-02 7.85981536e-01 -2.01899052e+00 4.54253703e-01 3.01032543e-01 8.74997497e-01 1.30940259e-01 9.23768103e-01 3.21004599e-01 1.03123999e+00 -3.08831841e-01 -1.76339880e-01 2.66093463e-01 7.61456788e-02 -5.83871007e-01 7.18736827e-01 7.44510472e-01 -6.76612079e-01 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e4ee51ad-9862-4951-867a-13e4dad19582	lip-reading-driven-deep-learning-approach-for	1808.00046	null	http://arxiv.org/abs/1808.00046v1	http://arxiv.org/pdf/1808.00046v1.pdf	Lip-Reading Driven Deep Learning Approach for Speech Enhancement	This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The proposed approach leverages the complementary strengths of both deep learning and analytical acoustic modelling (filtering based approach) as compared to recently published, comparatively simpler benchmark approaches that rely only on deep learning. The proposed audio-visual (AV) speech enhancement framework operates at two levels. In the first level, a novel deep learning-based lip-reading regression model is employed. In the second level, lip-reading approximated clean-audio features are exploited, using an enhanced, visually-derived Wiener filter (EVWF), for the clean audio power spectrum estimation. Specifically, a stacked long-short-term memory (LSTM) based lip-reading regression model is designed for clean audio features estimation using only temporal visual features considering different number of prior visual frames. For clean speech spectrum estimation, a new filterbank-domain EVWF is formulated, which exploits estimated speech features. The proposed EVWF is compared with conventional Spectral Subtraction and Log-Minimum Mean-Square Error methods using both ideal AV mapping and LSTM driven AV mapping. The potential of the proposed speech enhancement framework is evaluated under different dynamic real-world commercially-motivated scenarios (e.g. cafe, public transport, pedestrian area) at different SNR levels (ranging from low to high SNRs) using benchmark Grid and ChiME3 corpora. For objective testing, perceptual evaluation of speech quality is used to evaluate the quality of restored speech. For subjective testing, the standard mean-opinion-score method is used with inferential statistics. Comparative simulation results demonstrate significant lip-reading and speech enhancement improvement in terms of both speech quality and speech intelligibility.	['Mandar Gogate', 'William M. Whitmer', 'Amir Hussain', 'Ahsan Adeel']	2018-07-31	null	null	null	null	['acoustic-modelling']	['speech']	[ 3.95256996e-01 -2.74473310e-01 9.38843861e-02 -1.72221400e-02 -1.49919689e+00 -8.29463676e-02 5.49257994e-01 1.66599840e-01 -3.97222310e-01 7.86487103e-01 7.08054066e-01 -2.36099020e-01 -2.68832803e-01 -3.74518633e-01 -5.58719099e-01 -9.35940623e-01 4.17697579e-02 -7.05052376e-01 -6.98491633e-02 -1.04551651e-01 2.22149730e-01 3.96553814e-01 -1.80641270e+00 1.94340512e-01 8.32087636e-01 1.36476123e+00 4.45102185e-01 1.12569308e+00 7.69797042e-02 4.72935081e-01 -6.99715137e-01 -2.42586359e-01 1.15685292e-01 -2.04801738e-01 -2.63600349e-01 3.54577899e-02 2.90526569e-01 -4.52397466e-01 -2.40324914e-01 1.23442245e+00 1.22854340e+00 4.04634953e-01 5.16307175e-01 -1.04669785e+00 -4.52641129e-01 1.06097490e-01 -4.75396812e-01 1.53335065e-01 5.34736335e-01 4.70010698e-01 7.34018266e-01 -1.21452785e+00 1.68224275e-01 1.18456590e+00 8.19489717e-01 3.36341918e-01 -1.14436972e+00 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fde48f96-69c1-4e9d-aa6e-4432dc868f76	iiit-dwd-eacl2021-identifying-troll-meme-in	null	null	https://aclanthology.org/2021.dravidianlangtech-1.33	https://aclanthology.org/2021.dravidianlangtech-1.33.pdf	IIIT_DWD@EACL2021: Identifying Troll Meme in Tamil using a hybrid deep learning approach	Social media are an open forum that allows people to share their knowledge, abilities, talents, ideas, or expressions. Simultaneously, it also allows people to post disrespectful, trolling, defamation, or negative content targeting users or the community based on their gender, race, religious beliefs, etc. Such posts are available in the form of text, image, video, and meme. Among them, memes are currently widely used to disseminate offensive material amongst people. It is primarily in the form of pictures and text. In the present paper, troll memes are identified, which is necessary to create a healthy society. To do so, a hybrid deep learning model combining convolutional neural networks and bidirectional long short term memory is proposed to identify trolled memes. The dataset used in the study is a part of the competition EACL 2021: Troll Meme classification in Tamil. The proposed model obtained 10th rank in the competition and reported a precision of 0.52, recall 0.59, and weighted F10.3.	['Sunil Saumya', 'Ankit Kumar Mishra']	null	null	null	null	eacl-dravidianlangtech-2021-4	['meme-classification']	['natural-language-processing']	[-3.86567831e-01 -3.41526210e-01 -2.53278166e-01 2.05084234e-01 -2.50608742e-01 -5.17713785e-01 9.02182996e-01 7.08488941e-01 -5.70136189e-01 7.76067078e-01 4.79278624e-01 6.38960376e-02 2.56883949e-01 -8.29560101e-01 -2.38423690e-01 -5.35913587e-01 4.11269665e-01 -2.31834557e-02 1.33625478e-01 -6.37646794e-01 1.13705075e+00 2.53290623e-01 -1.14460135e+00 4.56360132e-01 5.55014014e-01 7.17622936e-01 -1.94167733e-01 5.40704787e-01 -4.78061050e-01 1.28954887e+00 -8.29781771e-01 -6.12907708e-01 -2.78817952e-01 -3.42167586e-01 -6.67706549e-01 -1.32391289e-01 6.08982682e-01 -3.72815639e-01 -2.60279983e-01 1.03344047e+00 5.48632264e-01 3.26492973e-02 6.47760153e-01 -8.34545434e-01 -7.46398389e-01 3.50288033e-01 -7.57677436e-01 5.10747254e-01 5.09531260e-01 -2.76050299e-01 5.85401237e-01 -7.47316778e-01 5.67628205e-01 1.30471718e+00 3.57436657e-01 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aff3886d-7655-45d1-8b96-db5ac0119876	commonsense-for-generative-multi-hop-question	1809.06309	null	https://arxiv.org/abs/1809.06309v3	https://arxiv.org/pdf/1809.06309v3.pdf	Commonsense for Generative Multi-Hop Question Answering Tasks	Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model's performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show promising initial results of the generalizability of our background knowledge enhancements by demonstrating some improvement on QAngaroo-WikiHop, another multi-hop reasoning dataset.	['Lisa Bauer', 'Yicheng Wang', 'Mohit Bansal']	2018-09-17	commonsense-for-generative-multi-hop-question-1	https://aclanthology.org/D18-1454	https://aclanthology.org/D18-1454.pdf	emnlp-2018-10	['multi-hop-question-answering', 'implicit-relations']	['knowledge-base', 'natural-language-processing']	[ 3.58480573e-01 7.77948678e-01 -1.09833628e-01 -2.45146140e-01 -1.57627916e+00 -6.92102313e-01 8.65997195e-01 3.34879845e-01 -1.69570848e-01 1.05699098e+00 9.88143086e-01 -4.76722360e-01 -1.05501615e-01 -1.18677878e+00 -8.11687350e-01 -2.12223396e-01 3.72685730e-01 1.12533450e+00 2.21428677e-01 -7.90617049e-01 3.75125170e-01 -2.55339295e-01 -1.50018871e+00 8.27589035e-01 1.37369514e+00 4.97523397e-01 2.95418147e-02 6.46163642e-01 -2.14069963e-01 1.59409988e+00 -8.67875397e-01 -9.68031228e-01 -1.45602748e-01 -9.01585579e-01 -1.54680562e+00 -5.31061113e-01 5.83356917e-01 -3.82792860e-01 -3.64276826e-01 6.36103511e-01 4.32081908e-01 4.78539854e-01 6.88593149e-01 -9.97173667e-01 -1.20320702e+00 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18f79f9d-ce3c-4774-8a6a-157e97d4e949	phase-slam-phase-based-simultaneous	2201.09048	null	https://arxiv.org/abs/2201.09048v1	https://arxiv.org/pdf/2201.09048v1.pdf	Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems	Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction. The novelty of this work is threefold: (1) developing a reprojection model from 3D points to 2D phase data towards phase registration with low computational complexity; (2) developing a local optimizer to achieve SLI sensor pose estimation (odometry) using the derived Jacobian matrix for the 6 DoF variables; (3) developing a compressive phase comparison method to achieve high-efficiency loop closure detection. The whole Phase-SLAM pipeline is then exploited using existing global pose graph optimization techniques. We build datasets from both the unreal simulation platform and a robotic arm based SLI system in real-world to verify the proposed approach. The experiment results demonstrate that the proposed Phase-SLAM outperforms other state-of-the-art methods in terms of the efficiency and accuracy of pose estimation and 3D reconstruction. The open-source code is available at https://github.com/ZHENGXi-git/Phase-SLAM.	['Qi Hao', 'Rui Gao', 'Rui Ma', 'Xi Zheng']	2022-01-22	null	null	null	null	['3d-object-reconstruction', 'object-reconstruction', 'loop-closure-detection']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.62361735e-01 -1.94289684e-01 1.35317177e-01 -3.58398557e-01 -9.52840567e-01 -4.67612326e-01 4.63994890e-01 9.85978357e-03 -3.05929989e-01 5.39119303e-01 -3.06313902e-01 -2.92279184e-01 -3.88938993e-01 -7.68391609e-01 -9.77191329e-01 -5.17457485e-01 -9.37044472e-02 9.70850050e-01 8.94075036e-02 -1.61470488e-01 4.89967227e-01 6.68144345e-01 -1.30861020e+00 -8.23523462e-01 8.45142245e-01 9.11830187e-01 4.35150802e-01 5.12297332e-01 3.03752035e-01 8.20163116e-02 -1.22343637e-01 3.96213263e-01 5.56323171e-01 -1.33206218e-01 -2.24092633e-01 -8.22685063e-02 6.13672435e-01 -1.01617254e-01 -1.02635592e-01 1.02540362e+00 6.20594203e-01 -5.75182401e-02 1.32658109e-01 -1.31362367e+00 3.07431281e-01 -4.01839107e-01 -7.09952354e-01 -5.88585734e-01 8.93934488e-01 7.11262673e-02 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99acc428-4b9b-41c7-b99a-bf8ea34371a2	investigating-tradeoffs-in-real-world-video	2111.12704	null	https://arxiv.org/abs/2111.12704v1	https://arxiv.org/pdf/2111.12704v1.pdf	Investigating Tradeoffs in Real-World Video Super-Resolution	The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage indispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency. Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length tradeoff. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40\% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset will be made publicly available.	['Chen Change Loy', 'Xiangyu Xu', 'Shangchen Zhou', 'Kelvin C. K. Chan']	2021-11-24	null	http://openaccess.thecvf.com//content/CVPR2022/html/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.pdf	cvpr-2022-1	['video-super-resolution']	['computer-vision']	[ 3.30089450e-01 -4.33468550e-01 -9.97810215e-02 -3.23015571e-01 -7.91003942e-01 -4.57893074e-01 2.99602121e-01 -3.32694978e-01 -2.54376471e-01 8.13316345e-01 2.80045420e-01 -1.16431981e-01 1.24301150e-01 -5.50530076e-01 -7.65966237e-01 -6.82027817e-01 -1.42000794e-01 -2.41251722e-01 4.21733379e-01 -2.26216719e-01 7.11894631e-02 4.48962778e-01 -1.72870374e+00 3.72834384e-01 1.06618679e+00 1.01533902e+00 5.30655444e-01 5.80317199e-01 1.08207695e-01 7.85936892e-01 -6.71759188e-01 -3.82675558e-01 5.04516184e-01 -3.67677808e-01 -3.74362558e-01 2.86358267e-01 7.54013240e-01 -4.90370095e-01 -4.46602613e-01 1.15807438e+00 6.66258931e-01 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a9b0514f-5df5-44b9-aa76-eb85fee06a87	a-sparse-learning-approach-to-the-design-of	2004.13164	null	http://arxiv.org/abs/2004.13164v1	http://arxiv.org/pdf/2004.13164v1.pdf	A Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties	This paper considers the design of tunable decision schemes capable of rejecting with high probability mismatched signals embedded in Gaussian interference with unknown covariance matrix. To this end, a sparse recovery technique is exploited to enhance the resolution at which the target angle of arrival is estimated with the objective to obtain high-selective detectors. The outcomes of this estimation procedure are used to devise detection architectures relying on either the twostage design paradigm or heuristic design procedures based upon the generalized likelihood ratio test. Remarkably, the new decision rules exhibit a bounded-constant false alarm rate property and allow for a tradeoff between the matched detection performance and the rejection of undesired signals by tuning a design parameter. At the analysis stage, the performance of the newly proposed detectors is assessed also in comparison with existing selective competitors. The results show that the new detectors can outperform the considered counterparts in terms of rejection of unwanted signals, while retaining reasonable detection performance of matched signals.	[]	2020-04-27	null	null	null	null	['sparse-learning']	['methodology']	[ 5.71782887e-01 -8.68111253e-02 -9.29408986e-03 -1.04014568e-01 -7.61283994e-01 -5.90446949e-01 6.87282979e-01 1.16698973e-01 -3.32369596e-01 6.89133167e-01 -1.55212879e-01 -2.23508269e-01 -7.16962516e-01 -4.75496024e-01 -9.65237096e-02 -1.06681681e+00 -2.49849260e-02 2.10894290e-02 9.06972289e-02 3.75685468e-02 2.75295973e-01 9.01778758e-01 -1.58727777e+00 -2.43312031e-01 8.21635962e-01 1.22434974e+00 -1.50594234e-01 5.76049626e-01 5.16790330e-01 2.16313213e-01 -6.12701476e-01 2.74726041e-02 3.89311701e-01 -3.19785237e-01 4.49338853e-01 4.49108183e-02 1.59386605e-01 1.45069987e-01 -1.35577530e-01 1.15655529e+00 5.65078676e-01 5.64844385e-02 9.61570024e-01 -9.00655031e-01 1.33873345e-02 2.61852950e-01 -4.76028442e-01 4.86931622e-01 4.86160517e-01 -1.78830117e-01 7.49410331e-01 -1.36827981e+00 1.79505292e-02 6.87601149e-01 3.77827048e-01 -1.15819694e-02 -1.29995263e+00 -6.85887694e-01 -2.84996271e-01 -4.07853462e-02 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33045bb8-9e99-448e-af3f-58a2a19ff22f	a-rule-based-bpso-approach-to-produce-low	2111.12802	null	https://arxiv.org/abs/2111.12802v1	https://arxiv.org/pdf/2111.12802v1.pdf	A Rule-based/BPSO Approach to Produce Low-dimensional Semantic Basis Vectors Set	We intend to generate low-dimensional explicit distributional semantic vectors. In explicit semantic vectors, each dimension corresponds to a word, so word vectors are interpretable. In this research, we propose a new approach to obtain low-dimensional explicit semantic vectors. First, the proposed approach considers the three criteria Word Similarity, Number of Zero, and Word Frequency as features for the words in a corpus. Then, we extract some rules for obtaining the initial basis words using a decision tree that is drawn based on the three features. Second, we propose a binary weighting method based on the Binary Particle Swarm Optimization algorithm that obtains N_B = 1000 context words. We also use a word selection method that provides N_S = 1000 context words. Third, we extract the golden words of the corpus based on the binary weighting method. Then, we add the extracted golden words to the context words that are selected by the word selection method as the golden context words. We use the ukWaC corpus for constructing the word vectors. We use MEN, RG-65, and SimLex-999 test sets to evaluate the word vectors. We report the results compared to a baseline that uses 5k most frequent words in the corpus as context words. The baseline method uses a fixed window to count the co-occurrences. We obtain the word vectors using the 1000 selected context words together with the golden context words. Our approach compared to the Baseline method increases the Spearman correlation coefficient for the MEN, RG-65, and SimLex-999 test sets by 4.66%, 14.73%, and 1.08%, respectively.	['Morteza Analoui', 'Atefe Pakzad']	2021-11-24	null	null	null	null	['word-similarity']	['natural-language-processing']	[ 2.45566927e-02 -3.73478979e-01 -1.08512618e-01 -3.03814560e-01 -2.80106187e-01 -4.73339558e-01 6.31177008e-01 3.35872084e-01 -1.01554322e+00 6.90791667e-01 4.78267372e-01 -1.42360568e-01 -3.51289481e-01 -1.06449485e+00 -1.17688127e-01 -7.01689959e-01 9.09234881e-02 3.23988706e-01 1.83128923e-01 -5.04401088e-01 7.56523252e-01 7.75273144e-02 -1.91403663e+00 1.60414919e-01 9.80419338e-01 8.80661011e-01 6.44001007e-01 4.48012143e-01 -8.26596856e-01 -1.40948310e-01 -8.78410399e-01 -1.76200375e-01 -2.88498774e-02 -4.81165051e-01 -5.85089028e-01 -1.28199518e-01 -3.43183905e-01 2.08247602e-01 2.40441605e-01 1.08855975e+00 4.29946512e-01 6.44668877e-01 9.30239141e-01 -1.01181459e+00 -7.59809315e-01 6.04757190e-01 -4.19314146e-01 -5.89890126e-03 3.09876591e-01 -3.10677618e-01 1.19439709e+00 -1.14069545e+00 5.69445908e-01 1.45015562e+00 3.57547790e-01 3.65070850e-01 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-5.66949010e-01 7.36792862e-01 -2.34065846e-01 -1.01997495e+00 -7.39087880e-01 -2.78033435e-01]	[10.252334594726562, 8.908693313598633]
cc3216c2-a102-4167-b2b5-c1fc65224458	urban-sound-tagging-using-convolutional	1909.12699	null	https://arxiv.org/abs/1909.12699v1	https://arxiv.org/pdf/1909.12699v1.pdf	Urban Sound Tagging using Convolutional Neural Networks	In this paper, we propose a framework for environmental sound classification in a low-data context (less than 100 labeled examples per class). We show that using pre-trained image classification models along with the usage of data augmentation techniques results in higher performance over alternative approaches. We applied this system to the task of Urban Sound Tagging, part of the DCASE 2019. The objective was to label different sources of noise from raw audio data. A modified form of MobileNetV2, a convolutional neural network (CNN) model was trained to classify both coarse and fine tags jointly. The proposed model uses log-scaled Mel-spectrogram as the representation format for the audio data. Mixup, Random erasing, scaling, and shifting are used as data augmentation techniques. A second model that uses scaled labels was built to account for human errors in the annotations. The proposed model achieved the first rank on the leaderboard with Micro-AUPRC values of 0.751 and 0.860 on fine and coarse tags, respectively.	['Sainath Adapa']	2019-09-27	null	null	null	null	['environmental-sound-classification', 'sound-classification']	['audio', 'audio']	[ 2.67237037e-01 -1.30603611e-01 3.88698757e-01 -3.97329122e-01 -1.10325301e+00 -5.66717863e-01 5.32590747e-01 3.01500529e-01 -9.12381172e-01 5.12429833e-01 2.91457653e-01 -8.80369842e-02 1.69579059e-01 -7.72025764e-01 -8.76057386e-01 -5.10022163e-01 -1.43520564e-01 4.51235026e-02 4.87452567e-01 -7.28194043e-02 -2.86882427e-02 2.19892234e-01 -2.00243831e+00 5.18254519e-01 4.16878134e-01 1.44306326e+00 3.68860960e-01 1.23553729e+00 -3.26533131e-02 1.15783536e+00 -8.36756110e-01 -8.61726701e-02 3.77117187e-01 4.85211499e-02 -8.33653629e-01 -4.32944030e-01 8.97192597e-01 -5.02036326e-02 -1.56797722e-01 1.05937970e+00 9.22870696e-01 3.40304047e-01 3.67229640e-01 -1.22465372e+00 -2.46398702e-01 1.02758741e+00 -1.32024838e-02 2.58417577e-01 4.13022824e-02 -3.45250815e-01 1.06401837e+00 -8.30092371e-01 5.24413213e-02 9.58140373e-01 1.23183870e+00 4.56551552e-01 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-7.13808060e-01 3.05404663e-01 5.42436004e-01 -6.34313762e-01 -3.27033699e-01 5.08149445e-01]	[15.202110290527344, 5.15608549118042]
13a4c3a5-63c7-4d8d-9221-436bdf18c28e	cora-adapting-clip-for-open-vocabulary	2303.13076	null	https://arxiv.org/abs/2303.13076v1	https://arxiv.org/pdf/2303.13076v1.pdf	CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching	Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks; (2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA$^+$ on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA$^+$ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark.	['Hongsheng Li', 'Rui Zhao', 'Feng Zhu', 'Xiaoshi Wu']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.pdf	cvpr-2023-1	['open-vocabulary-object-detection']	['computer-vision']	[ 9.00782347e-02 -3.98277491e-02 -2.81933337e-01 -1.66798845e-01 -1.32673740e+00 -8.68717611e-01 6.61701739e-01 3.62368554e-01 -6.11830294e-01 1.30821407e-01 -2.35873327e-01 -2.66749799e-01 5.51366031e-01 -4.92006332e-01 -1.01648903e+00 -5.10020077e-01 -8.34186822e-02 3.16127002e-01 8.92103314e-01 8.08769390e-02 1.93381719e-02 7.16445684e-01 -1.78863251e+00 5.70611298e-01 3.15270126e-01 1.47599494e+00 3.48572224e-01 6.57990396e-01 -1.72182560e-01 2.98406422e-01 -5.81226230e-01 -8.86932686e-02 6.36922538e-01 1.02088213e-01 -3.78539532e-01 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a4356c23-9f2e-4b2c-99ca-92daabb48777	rasa-relation-and-sensitivity-aware	2305.13653	null	https://arxiv.org/abs/2305.13653v1	https://arxiv.org/pdf/2305.13653v1.pdf	RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search	Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For one thing, existing methods cluster representations of all positive pairs without distinction and overlook the noise problem caused by the weak positive pairs where the text and the paired image have noise correspondences, thus leading to overfitting learning. RA offsets the overfitting risk by introducing a novel positive relation detection task (i.e., learning to distinguish strong and weak positive pairs). For another thing, learning invariant representation under data augmentation (i.e., being insensitive to some transformations) is a general practice for improving representation's robustness in existing methods. Beyond that, we encourage the representation to perceive the sensitive transformation by SA (i.e., learning to detect the replaced words), thus promoting the representation's robustness. Experiments demonstrate that RaSa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. Code is available at: https://github.com/Flame-Chasers/RaSa.	['Min Zhang', 'Liqiang Nie', 'Zhenfeng Fan', 'Chen Chen', 'Ziqiang Cao', 'Daming Gao', 'Min Cao', 'Yang Bai']	2023-05-23	null	null	null	null	['person-search']	['computer-vision']	[ 2.88059503e-01 -5.23523353e-02 -2.00902238e-01 -2.51430780e-01 -9.79768276e-01 -4.43818092e-01 8.93077016e-01 5.79303838e-02 -4.15966302e-01 5.58661759e-01 4.78498250e-01 2.49841958e-01 -2.02807292e-01 -7.32119858e-01 -4.93745923e-01 -7.77213454e-01 3.23577970e-01 5.26325941e-01 -5.97838014e-02 -3.21909457e-01 -1.42473787e-01 -1.05715729e-02 -1.46877551e+00 4.63504016e-01 8.57087612e-01 9.28640127e-01 -1.38533503e-01 1.90082058e-01 2.63269961e-01 6.75868511e-01 -5.06602287e-01 -7.63045430e-01 2.40983829e-01 -4.15853053e-01 -8.73073936e-01 6.88762665e-02 3.70748311e-01 -1.26568720e-01 -6.98525012e-01 1.16737926e+00 6.62834108e-01 3.75604808e-01 7.68592596e-01 -1.33122587e+00 -1.16151989e+00 4.61407185e-01 -9.74049151e-01 2.43608460e-01 6.34131253e-01 1.55553252e-01 1.20459569e+00 -1.14686990e+00 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765ee641-7b2d-4bb9-a166-7292883c584a	s-2sql-injecting-syntax-to-question-schema	null	null	https://aclanthology.org/2022.findings-acl.99	https://aclanthology.org/2022.findings-acl.99.pdf	S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers	The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S^2SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network’s performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.	['Yongbin Li', 'Jian Sun', 'Bowen Li', 'Yanyang Li', 'Bowen Qin', 'Lihan Wang', 'Ruiying Geng', 'Binyuan Hui']	null	null	null	null	findings-acl-2022-5	['text-to-sql']	['computer-code']	[ 4.36222628e-02 5.09166598e-01 -3.19061697e-01 -7.25416064e-01 -7.94605434e-01 -7.07358181e-01 3.48286122e-01 2.92376339e-01 -9.37874541e-02 5.36906496e-02 3.13646019e-01 -9.27342832e-01 6.20479472e-02 -1.24232709e+00 -1.17032158e+00 3.38431180e-01 1.14073120e-01 4.99641538e-01 6.58243060e-01 -5.74377120e-01 8.64981487e-02 3.76760177e-02 -1.21500301e+00 4.22523201e-01 9.24379587e-01 1.02613306e+00 2.08220541e-01 5.71822703e-01 -9.00799513e-01 1.55638826e+00 -3.71782809e-01 -1.19764161e+00 6.07921630e-02 -2.37867251e-01 -1.05809546e+00 -4.31737632e-01 6.29158676e-01 -4.50033128e-01 -4.50600773e-01 9.70324218e-01 1.07288450e-01 -4.11403328e-01 -1.70561839e-02 -1.19499123e+00 -7.65121460e-01 8.96494091e-01 -1.18844062e-01 -1.35397822e-01 5.46624541e-01 -6.95678918e-03 1.80118477e+00 -6.77287698e-01 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c3e02ba1-5cdb-40ab-a94c-4742aa1fb226	graph-and-temporal-convolutional-networks-for	2012.11806	null	https://arxiv.org/abs/2012.11806v3	https://arxiv.org/pdf/2012.11806v3.pdf	Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos	Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs) to robustly estimate camera-centric multi-person 3D poses that do not require camera parameters. In particular, we introduce a human-joint GCN, which, unlike the existing GCN, is based on a directed graph that employs the 2D pose estimator's confidence scores to improve the pose estimation results. We also introduce a human-bone GCN, which models the bone connections and provides more information beyond human joints. The two GCNs work together to estimate the spatial frame-wise 3D poses and can make use of both visible joint and bone information in the target frame to estimate the occluded or missing human-part information. To further refine the 3D pose estimation, we use our temporal convolutional networks (TCNs) to enforce the temporal and human-dynamics constraints. We use a joint-TCN to estimate person-centric 3D poses across frames, and propose a velocity-TCN to estimate the speed of 3D joints to ensure the consistency of the 3D pose estimation in consecutive frames. Finally, to estimate the 3D human poses for multiple persons, we propose a root-TCN that estimates camera-centric 3D poses without requiring camera parameters. Quantitative and qualitative evaluations demonstrate the effectiveness of the proposed method.	['Robby T. Tan', 'Bo Yang', 'Bo wang', 'Yu Cheng']	2020-12-22	null	null	null	null	['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation', '3d-multi-person-pose-estimation', '3d-absolute-human-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-4.35081929e-01 -1.26656651e-01 3.90292183e-02 -8.63630697e-02 -2.92920530e-01 -1.96153790e-01 3.41884643e-01 -4.75436985e-01 -4.67103451e-01 5.71429372e-01 3.55431706e-01 4.67132479e-01 1.49426581e-02 -5.95653772e-01 -6.02910876e-01 -4.07880306e-01 -3.97091322e-02 5.64573824e-01 4.76585329e-01 -1.00929514e-01 -2.35981047e-01 6.49401069e-01 -1.26251066e+00 -2.75680393e-01 5.43488026e-01 7.33410120e-01 -4.42136936e-02 7.28209496e-01 5.02667606e-01 3.97808105e-01 -6.58140063e-01 -2.85619795e-01 3.56330067e-01 -2.32397720e-01 -2.04055578e-01 5.71311712e-01 6.67530417e-01 -7.23632455e-01 -6.88212514e-01 8.59776676e-01 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72c819b0-cd37-4e50-b490-f29338a3c3bb	representation-based-meta-learning-for-few	2106.15238	null	https://arxiv.org/abs/2106.15238v1	https://arxiv.org/pdf/2106.15238v1.pdf	Representation based meta-learning for few-shot spoken intent recognition	Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick customization of personal devices to new intents. This paper presents a few-shot spoken intent classification approach with task-agnostic representations via meta-learning paradigm. Specifically, we leverage the popular representation-based meta-learning learning to build a task-agnostic representation of utterances, that then use a linear classifier for prediction. We evaluate three such approaches on our novel experimental protocol developed on two popular spoken intent classification datasets: Google Commands and the Fluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel classes, the proposed framework provides an average classification accuracy of 88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent Speech Commands dataset. The performance is comparable to traditionally supervised classification models with abundant training samples.	['Brian Kingsbury', 'Karthik Sankaranarayanan', 'Saneem Chemmengath', 'Shreya Khare', 'Samarth Bharadwaj', 'Ashish Mittal']	2021-06-29	null	null	null	null	['intent-recognition']	['natural-language-processing']	[ 3.58801782e-01 -2.59621650e-01 -3.25574607e-01 -8.67333114e-01 -7.53707290e-01 -3.46515298e-01 7.89548874e-01 1.14545643e-01 -3.96692336e-01 3.77998471e-01 4.90927130e-01 -1.22128583e-01 2.40642473e-01 -4.20715988e-01 2.84844544e-03 -2.94336259e-01 -1.40556945e-02 4.09930348e-01 -9.49159339e-02 -3.93026620e-01 7.48008668e-01 -7.03728199e-02 -2.05353999e+00 6.26340747e-01 1.02927577e+00 1.16356945e+00 3.66804928e-01 9.29320633e-01 -6.22605801e-01 9.98046875e-01 -6.74680591e-01 1.13979436e-01 -3.17784905e-01 -1.74990878e-01 -8.23884666e-01 -2.17929691e-01 1.02259964e-01 -4.36670572e-01 -1.81766838e-01 6.49540663e-01 5.57840228e-01 4.88009095e-01 9.04606342e-01 -1.46441889e+00 -4.51704621e-01 6.99122310e-01 3.54548059e-02 1.89701229e-01 1.06914890e+00 1.07740268e-01 7.88071692e-01 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6018b00c-42fe-40b3-b0dc-7bcb2844112a	multimodal-self-supervised-learning-for	1912.05396	null	https://arxiv.org/abs/1912.05396v2	https://arxiv.org/pdf/1912.05396v2.pdf	Multimodal Self-Supervised Learning for Medical Image Analysis	Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learning. In this paper, we propose a novel self-supervised method that leverages multiple imaging modalities. We introduce the multimodal puzzle task, which facilitates rich representation learning from multiple image modalities. The learned representations allow for subsequent fine-tuning on different downstream tasks. To achieve that, we learn a modality-agnostic feature embedding by confusing image modalities at the data-level. Together with the Sinkhorn operator, with which we formulate the puzzle solving optimization as permutation matrix inference instead of classification, they allow for efficient solving of multimodal puzzles with varying levels of complexity. In addition, we also propose to utilize cross-modal generation techniques for multimodal data augmentation used for training self-supervised tasks. In other words, we exploit synthetic images for self-supervised pretraining, instead of downstream tasks directly, in order to circumvent quality issues associated with synthetic images, while improving data-efficiency and representations quality. Our experimental results, which assess the gains in downstream performance and data-efficiency, show that solving our multimodal puzzles yields better semantic representations, compared to treating each modality independently. Our results also highlight the benefits of exploiting synthetic images for self-supervised pretraining. We showcase our approach on four downstream tasks: Brain tumor segmentation and survival days prediction using four MRI modalities, Prostate segmentation using two MRI modalities, and Liver segmentation using unregistered CT and MRI modalities. We outperform many previous solutions, and achieve results competitive to state-of-the-art.	['Moin Nabi', 'Aiham Taleb', 'Christoph Lippert', 'Tassilo Klein']	2019-12-11	null	null	null	null	['liver-segmentation']	['medical']	[ 6.20802224e-01 3.80834460e-01 -3.08823556e-01 -3.12096804e-01 -1.35571015e+00 -7.59201229e-01 5.41790247e-01 1.07651576e-02 -4.08017337e-01 6.20970607e-01 3.84759426e-01 -8.03559721e-02 -4.06559885e-01 -6.67535841e-01 -8.88792217e-01 -8.22706699e-01 -8.65328535e-02 7.12629378e-01 -1.64773598e-01 9.64550748e-02 -3.43887955e-02 2.55952299e-01 -1.17255795e+00 7.46778667e-01 8.67244720e-01 9.60960805e-01 3.22577327e-01 6.22680783e-01 -5.37563972e-02 7.10267484e-01 -2.12823570e-01 -3.89899760e-01 3.34615111e-01 -4.47543532e-01 -9.36362088e-01 5.01867235e-01 1.60679266e-01 -1.06654018e-01 -3.07486326e-01 6.31877542e-01 3.01496387e-01 -1.22559749e-01 8.66529167e-01 -1.32999945e+00 -7.05949247e-01 6.05347037e-01 -7.56138861e-01 -4.37395126e-02 8.90571699e-02 4.70905125e-01 1.04665911e+00 -5.70266426e-01 6.98343158e-01 7.85221875e-01 7.32262015e-01 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d19a9d79-f253-4c3e-ad98-22e0833321db	ontology-based-technical-text-annotation	null	null	https://aclanthology.org/W14-6003	https://aclanthology.org/W14-6003.pdf	Ontology-based Technical Text Annotation	null	["Fran{\\c{c}}ois L{\\'e}vy", 'Yue Ma', 'Nadi Tomeh']	2014-08-01	null	null	null	ws-2014-8	['text-annotation']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 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bc2eaec3-065e-4805-9059-d7aa64df1210	tree-based-subgroup-discovery-in-electronic	2208.14329	null	https://arxiv.org/abs/2208.14329v1	https://arxiv.org/pdf/2208.14329v1.pdf	Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies	The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs.	['Jon A. Steingrimsson', 'Joseph W. Hogan', 'Allison Delong', 'Monicah Nyambura', 'Issa J. Dahabreh', 'Rami Kantor', 'Ann W. Mwangi', 'Jiabei Yang']	2022-08-30	null	null	null	null	['subgroup-discovery']	['methodology']	[ 3.22227508e-01 -3.80063891e-01 -1.15408087e+00 -6.30908191e-01 -6.54926300e-01 -3.81850094e-01 2.38352105e-01 6.70119882e-01 -2.02127278e-01 9.51504052e-01 9.33396161e-01 -7.12453663e-01 -3.62890333e-01 -6.76941991e-01 -2.96392769e-01 -2.30705723e-01 -7.93327689e-01 6.84401989e-01 -5.52112103e-01 3.92352998e-01 -1.30658582e-01 1.80479392e-01 -7.25273430e-01 4.53667074e-01 1.00514746e+00 -6.89924955e-02 -2.19569564e-01 2.52681345e-01 1.57432303e-01 3.71924758e-01 2.40330338e-01 3.62713682e-03 -2.09568769e-01 -5.50955117e-01 -2.98015386e-01 -1.00445002e-01 4.11808908e-01 -5.67726910e-01 -2.10542873e-01 3.82773936e-01 7.74419725e-01 -3.83914888e-01 9.30662274e-01 -1.39105928e+00 -5.70739150e-01 7.69863248e-01 -7.72842467e-01 1.45043015e-01 3.75401318e-01 3.36394578e-01 6.23666525e-01 -6.59974515e-01 8.97717059e-01 1.67848337e+00 1.34145582e+00 5.86506486e-01 -2.06750178e+00 -8.05720031e-01 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5.31865835e-01 -2.99999148e-01 -1.74683377e-01 -1.71523392e-01]	[7.972390174865723, 5.492047309875488]
8375773a-ea4b-40cd-978a-5163394098cf	acute-ischemic-stroke-lesion-segmentation-in	2301.06793	null	https://arxiv.org/abs/2301.06793v1	https://arxiv.org/pdf/2301.06793v1.pdf	Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks	In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a specific patches sampling strategy was used to address the large size of medical images, to smooth out the effect of the class imbalance problem and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. Our proposed model achieves an average Dice of $0.628\pm0.033$, sensitivity of $0.699\pm0.039$, specificity of $0.9965\pm0.0016$ and precision of $0.619\pm0.036$, showing promising segmentation results.	['V. B. Berikov', 'A. A. Tulupov', 'Yu. N. Sinyavskiy', 'K. M. Sherman', 'I. A. Pestunov', 'S. K. Verbitskiy', 'A. V. Dobshik']	2023-01-17	null	null	null	null	['ischemic-stroke-lesion-segmentation']	['medical']	[-6.32956177e-02 1.84108704e-01 9.74874124e-02 -4.40251261e-01 -6.47633076e-01 -1.62423372e-01 8.22350755e-02 3.47274721e-01 -8.06885242e-01 9.38959479e-01 -1.08041756e-01 -2.96052456e-01 -4.63495731e-01 -8.50397110e-01 -4.79185313e-01 -6.57722890e-01 -5.18178403e-01 6.07802331e-01 2.54142761e-01 3.24333757e-01 2.31621429e-01 8.05919170e-01 -1.09716499e+00 4.81611192e-02 9.91542220e-01 1.12050903e+00 1.77574471e-01 4.01205510e-01 3.67236249e-02 5.77351332e-01 -4.13151473e-01 -3.54784653e-02 4.92502183e-01 -3.63882959e-01 -8.47832382e-01 1.08067989e-01 -5.38458936e-02 -5.20478368e-01 -2.57833421e-01 1.06085992e+00 7.27659166e-01 1.44490004e-01 8.48048806e-01 -7.33725369e-01 -3.85367751e-01 4.47565198e-01 -7.17786729e-01 6.15800917e-01 -3.04994017e-01 1.81272611e-01 3.82738292e-01 -6.48802161e-01 5.56005597e-01 6.37506604e-01 5.85838139e-01 2.98225552e-01 -9.00054038e-01 -7.65956581e-01 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a8845fa5-278d-490a-a058-ca58020f4cbc	adversarial-instance-augmentation-for	null	null	https://ieeexplore.ieee.org/document/9386248	https://ieeexplore.ieee.org/document/9386248	Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images	Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly focus on transformation-based global image augmentation and cost-sensitive algorithms. In this article, we propose a novel data-level solution, namely, Instance-level change Augmentation (IAug), to generate bitemporal images that contain changes involving plenty and diverse buildings by leveraging generative adversarial training. The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images. To achieve this, a building generator is employed to produce realistic building images that are consistent with the given layouts. Diverse styles are later transferred onto the generated images. We further propose context-aware blending for a realistic composite of the building and the background. We augment the existing CD data sets and also design a simple yet effective CD model—CD network (CDNet). Our method (CDNet + IAug) has achieved state-of-the-art results in two building CD data sets (LEVIR-CD and WHU-CD). Interestingly, we achieve comparable results with only 20% of the training data as the current state-of-the-art methods using 100% data. Extensive experiments have validated the effectiveness of the proposed IAug. Our augmented data set has a lower risk of class imbalance than the original one. Conventional learning on the synthesized data set outperforms several popular cost-sensitive algorithms on the original data set. Our code and data are available at https://github.com/justchenhao/IAug_CDNet .	['Zhenwei Shi', 'Wenyuan Li', 'Hao Chen']	2021-03-25	null	null	null	ieee-transactions-on-geoscience-and-remote-7	['image-augmentation', 'building-change-detection-for-remote-sensing']	['computer-vision', 'miscellaneous']	[ 4.40726250e-01 -2.93285578e-01 7.72846863e-02 -2.20943257e-01 -9.35305893e-01 -4.80472326e-01 5.40814459e-01 -3.27646017e-01 -3.00295209e-03 6.29469216e-01 4.47195396e-02 1.34335682e-01 1.62954509e-01 -1.28310812e+00 -1.05871892e+00 -8.35331976e-01 2.72987902e-01 3.36045146e-01 1.72634616e-01 -4.21724975e-01 2.91043846e-03 4.73680735e-01 -1.85744905e+00 2.05329061e-01 1.12910640e+00 7.83249915e-01 2.41697595e-01 6.65639281e-01 1.17183581e-01 4.11459714e-01 -6.06033981e-01 -4.41619992e-01 4.79580551e-01 -4.25481826e-01 -3.52539629e-01 4.34218943e-01 9.12958145e-01 -3.71073067e-01 -2.34517217e-01 9.30419445e-01 8.37619185e-01 1.32713411e-02 4.87219185e-01 -1.47675419e+00 -9.41090465e-01 2.25954920e-01 -8.35017145e-01 -5.15890829e-02 5.21514192e-02 3.49599123e-01 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106f866d-48d4-4f3f-997d-b165cd4f4c13	an-empirical-etudy-of-non-lexical-extensions	null	null	https://aclanthology.org/C12-2115	https://aclanthology.org/C12-2115.pdf	An Empirical Etudy of Non-Lexical Extensions to Delexicalized Transfer	null	['Anders S{\\o}gaard', 'Julie Wulff']	2012-12-01	an-empirical-etudy-of-non-lexical-extensions-1	https://aclanthology.org/C12-2115	https://aclanthology.org/C12-2115.pdf	coling-2012-12	['transition-based-dependency-parsing']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 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b3c52af2-d3c7-4e51-983c-96f0950ae6b4	learning-based-automatic-synthesis-of	2305.15642	null	https://arxiv.org/abs/2305.15642v2	https://arxiv.org/pdf/2305.15642v2.pdf	Learning-Based Automatic Synthesis of Software Code and Configuration	Increasing demands in software industry and scarcity of software engineers motivates researchers and practitioners to automate the process of software generation and configuration. Large scale automatic software generation and configuration is a very complex and challenging task. In this proposal, we set out to investigate this problem by breaking down automatic software generation and configuration into two different tasks. In first task, we propose to synthesize software automatically with input output specifications. This task is further broken down into two sub-tasks. The first sub-task is about synthesizing programs with a genetic algorithm which is driven by a neural network based fitness function trained with program traces and specifications. For the second sub-task, we formulate program synthesis as a continuous optimization problem and synthesize programs with covariance matrix adaption evolutionary strategy (a state-of-the-art continuous optimization method). Finally, for the second task, we propose to synthesize configurations of large scale software from different input files (e.g. software manuals, configurations files, online blogs, etc.) using a sequence-to-sequence deep learning mechanism.	['Shantanu Mandal']	2023-05-25	null	null	null	null	['program-synthesis']	['computer-code']	[ 5.35587370e-01 -1.77738026e-01 4.02186453e-01 -3.37834358e-01 -5.75747252e-01 -6.97871745e-01 1.32106125e-01 -2.73152278e-03 -1.02515578e-01 6.36373818e-01 -3.48721743e-01 -5.80918968e-01 -6.89539313e-02 -9.19439912e-01 -9.32578802e-01 -3.35298240e-01 2.78592914e-01 4.31715310e-01 -1.66213494e-02 -4.69171226e-01 7.95515060e-01 1.86782092e-01 -1.94279754e+00 1.98282018e-01 1.18446934e+00 5.69697559e-01 5.46393156e-01 8.94701302e-01 -3.11051130e-01 3.01413417e-01 -7.65266299e-01 -2.54370034e-01 2.64681786e-01 -8.20343852e-01 -6.87649131e-01 2.44669974e-01 2.91622002e-02 2.34492451e-01 4.25583601e-01 1.44918203e+00 5.10891855e-01 -1.32051349e-01 4.81409103e-01 -1.31439233e+00 -8.51468265e-01 9.71394837e-01 -2.62936234e-01 -2.42432639e-01 2.84658730e-01 4.14497137e-01 8.22543383e-01 -6.05047107e-01 5.78410089e-01 9.32986140e-01 5.18386245e-01 4.60216194e-01 -1.39099407e+00 -2.92627364e-01 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1.73766110e-02 1.22681372e-01 -3.05782080e-01 4.82197069e-02 3.69900972e-01]	[8.011943817138672, 7.428036212921143]
39a4f24b-4c31-437f-81ef-5040a58ec2d7	orgmining-2-0-a-novel-framework-for	2011.12445	null	https://arxiv.org/abs/2011.12445v2	https://arxiv.org/pdf/2011.12445v2.pdf	OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs	Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use of process mining offers a promising way to help address the need through utilizing event log data stored in information systems. By extracting knowledge about the actual behavior of resources participating in business processes from event logs, organizational models can be constructed, which facilitate the analysis of the de facto grouping of human resources relevant to process execution. Nevertheless, open research gaps remain to be addressed when applying the state-of-the-art process mining to analyze resource grouping. For one, the discovery of organizational models has only limited connections with the context of process execution. For another, a rigorous solution that evaluates organizational models against event log data is yet to be proposed. In this paper, we aim to tackle these research challenges by developing a novel framework built upon a richer definition of organizational models coupling resource grouping with process execution knowledge. By introducing notions of conformance checking for organizational models, the framework allows effective evaluation of organizational models, and therefore provides a foundation for analyzing and improving resource grouping based on event logs. We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.	['Yang Yu', 'Arthur H. M. ter Hofstede', 'Wil M. P. van der Aalst', 'Chun Ouyang', 'Jing Yang']	2020-11-24	null	null	null	null	['model-discovery']	['miscellaneous']	[ 3.01126093e-01 8.21862295e-02 -1.32611707e-01 -1.94084067e-02 1.26999050e-01 -2.88269699e-01 7.40371346e-01 9.94952738e-01 -3.08151931e-01 2.29717314e-01 3.95184420e-02 -3.46748173e-01 -6.01484239e-01 -1.21945393e+00 -5.06111048e-02 -2.12837547e-01 -2.89521009e-01 6.51233137e-01 3.94493759e-01 1.44690990e-01 4.87661839e-01 7.81901479e-01 -1.76651907e+00 2.58917063e-01 4.09573853e-01 9.51787710e-01 7.32964044e-03 4.14758950e-01 -3.86071771e-01 9.85292017e-01 -4.57760721e-01 7.33767822e-02 3.11443597e-01 -3.45649123e-01 -8.62044454e-01 3.36915135e-01 -3.65987033e-01 2.33401340e-02 2.35302567e-01 7.99971819e-01 -7.39219561e-02 2.81402171e-01 2.01670066e-01 -1.33165884e+00 3.57125439e-02 4.87877399e-01 -1.91280439e-01 3.82424116e-01 5.32014668e-01 1.68902963e-01 9.54043090e-01 -5.97193956e-01 7.56101668e-01 9.72831666e-01 4.10195440e-01 1.96758375e-01 -1.25229609e+00 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6.19009256e-01 -4.38939303e-01 -5.28144956e-01 -5.18535897e-02 2.90256232e-01]	[8.572782516479492, 6.020284652709961]
0b4143bd-225a-4af1-a098-18cfb38fb14d	the-mapkurator-system-a-complete-pipeline-for	2306.17059	null	https://arxiv.org/abs/2306.17059v2	https://arxiv.org/pdf/2306.17059v2.pdf	The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps	Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools like the Google Vision APIs for automatically transcribing text from these maps into machine-readable formats, they do not work well with large-sized images (e.g., high-resolution scanned documents), cannot infer the relation between the recognized text and other datasets, and are challenging to integrate with post-processing tools. This paper introduces the mapKurator system, an end-to-end system integrating machine learning models with a comprehensive data processing pipeline. mapKurator empowers automated extraction, post-processing, and linkage of text labels from large numbers of large-dimension historical map scans. The output data, comprising bounding polygons and recognized text, is in the standard GeoJSON format, making it easily modifiable within Geographic Information Systems (GIS). The proposed system allows users to quickly generate valuable data from large numbers of historical maps for in-depth analysis of the map content and, in turn, encourages map findability, accessibility, interoperability, and reusability (FAIR principles). We deployed the mapKurator system and enabled the processing of over 60,000 maps and over 100 million text/place names in the David Rumsey Historical Map collection. We also demonstrated a seamless integration of mapKurator with a collaborative web platform to enable accessing automated approaches for extracting and linking text labels from historical map scans and collective work to improve the results.	['Yao-Yi Chiang', 'Leeje Jang', 'Min Namgung', 'Yijun Lin', 'Zekun Li', 'Jina Kim']	2023-06-29	null	null	null	null	['zero-shot-learning']	['methodology']	[-2.15746075e-01 8.69212486e-03 1.88172892e-01 -4.32328314e-01 -1.06302297e+00 -1.13810527e+00 8.56664181e-01 6.55274272e-01 -4.45640951e-01 5.41617572e-01 4.20853227e-01 -5.61508298e-01 -3.56877416e-01 -1.34759820e+00 -6.90810442e-01 -2.54820675e-01 -5.90251498e-02 7.89976120e-01 3.23978812e-01 -4.00851667e-02 5.06803453e-01 7.00766206e-01 -1.57903302e+00 2.62911975e-01 9.44094002e-01 7.50362992e-01 6.02965951e-01 5.98885596e-01 -7.16046154e-01 3.95777553e-01 -5.26512444e-01 -3.95560324e-01 2.21442625e-01 3.71859610e-01 -8.90616357e-01 -5.35397410e-01 7.42145717e-01 -2.70714909e-01 -2.60881811e-01 8.35884929e-01 4.21493828e-01 -3.63938749e-01 5.47176540e-01 -1.00235546e+00 -7.54784644e-01 5.79014003e-01 -4.26808208e-01 2.40932971e-01 3.56809705e-01 -8.49640220e-02 8.40530992e-01 -7.72546053e-01 7.84444809e-01 9.57495928e-01 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d047c142-cf1d-4579-b3ee-a4cd6090bfec	aggressive-language-identification-using-word	null	null	https://aclanthology.org/W18-4414	https://aclanthology.org/W18-4414.pdf	Aggressive Language Identification Using Word Embeddings and Sentiment Features	This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.	['Constantin Or{\\u{a}}san']	2018-08-01	null	null	null	coling-2018-8	['aggression-identification']	['natural-language-processing']	[-2.44813144e-01 -5.80976121e-02 -1.86817452e-01 -2.45898083e-01 -1.38274029e-01 -1.83228850e-01 9.23156619e-01 4.86170650e-01 -1.15217745e+00 7.06402302e-01 5.43197334e-01 -3.34502868e-02 -2.82056630e-01 -7.33001590e-01 2.94811159e-01 -7.01209545e-01 1.67251565e-02 7.21849740e-01 3.54423404e-01 -7.91182518e-01 7.60958850e-01 4.68232751e-01 -1.51411748e+00 4.62610543e-01 3.50357533e-01 5.49189508e-01 -1.71046272e-01 8.30741942e-01 -5.90583146e-01 8.30728412e-01 -7.82115459e-01 -9.56866205e-01 6.08915240e-02 -1.54163942e-01 -1.14466882e+00 -3.23965251e-01 3.26340683e-02 -1.45902619e-01 -2.14527369e-01 8.28434110e-01 6.10349357e-01 2.20172122e-01 9.27368999e-01 -1.05199277e+00 -1.63832918e-01 6.32061899e-01 -2.26670936e-01 6.86908364e-01 8.53624523e-01 -3.95230129e-02 1.05614340e+00 -8.56970906e-01 5.29780686e-01 1.13842523e+00 8.99179101e-01 7.72414684e-01 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90690175-27ae-431b-b132-44f5c5638162	contrastive-learning-of-sentence	null	null	https://aclanthology.org/2021.icon-main.33	https://aclanthology.org/2021.icon-main.33.pdf	Contrastive Learning of Sentence Representations	Learning sentence representations which capture rich semantic meanings has been crucial for many NLP tasks. Pre-trained language models such as BERT have achieved great success in NLP, but sentence embeddings extracted directly from these models do not perform well without fine-tuning. We propose Contrastive Learning of Sentence Representations (CLSR), a novel approach which applies contrastive learning to learn universal sentence representations on top of pre-trained language models. CLSR utilizes semantic similarity of two sentences to construct positive instance for contrastive learning. Semantic information that has been captured by the pre-trained models is kept by getting sentence embeddings from these models with proper pooling strategy. An encoder followed by a linear projection takes these embeddings as inputs and is trained under a contrastive objective. To evaluate the performance of CLSR, we run experiments on a range of pre-trained language models and their variants on a series of Semantic Contextual Similarity tasks. Results show that CLSR gains significant performance improvements over existing SOTA language models.	['Ping Chen', 'Wei Ding', 'Hefei Qiu']	null	null	null	null	icon-2021-12	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[ 4.04823124e-01 5.74289002e-02 -2.52801478e-01 -6.21215641e-01 -7.22890556e-01 -2.34151229e-01 9.22661126e-01 4.54082072e-01 -7.50049591e-01 5.79098463e-01 7.63068557e-01 -1.58205509e-01 2.66824335e-01 -7.82429755e-01 -6.05728149e-01 -2.98058659e-01 -1.36708803e-02 2.81038702e-01 3.15693647e-01 -5.79555929e-01 4.41754818e-01 1.41905949e-01 -1.25276685e+00 7.97614753e-01 5.61795652e-01 7.14613795e-01 6.22958064e-01 7.88217068e-01 -7.42649376e-01 9.30618644e-01 -6.15784287e-01 -3.86503279e-01 -1.94836762e-02 -3.95382434e-01 -1.00235808e+00 -3.26639533e-01 2.12482587e-01 1.78269178e-01 -1.34722278e-01 8.62496436e-01 3.94522280e-01 3.73278737e-01 7.49414027e-01 -7.99449205e-01 -1.41321862e+00 9.07145143e-01 -3.48970354e-01 6.57124221e-01 4.26820338e-01 -1.09360769e-01 1.53200364e+00 -1.26086438e+00 4.87788290e-01 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c6eb98cf-9676-4ac6-be5e-96f175183979	a-cnn-based-approach-to-classify-cricket	1909.01228	null	https://arxiv.org/abs/1909.01228v1	https://arxiv.org/pdf/1909.01228v1.pdf	A CNN-based approach to classify cricket bowlers based on their bowling actions	With the advances in hardware technologies and deep learning techniques, it has become feasible to apply these techniques in diverse fields. Convolutional Neural Network (CNN), an architecture from the field of deep learning, has revolutionized Computer Vision. Sports is one of the avenues in which the use of computer vision is thriving. Cricket is a complex game consisting of different types of shots, bowling actions and many other activities. Every bowler, in a game of cricket, bowls with a different bowling action. We leverage this point to identify different bowlers. In this paper, we have proposed a CNN model to identify eighteen different cricket bowlers based on their bowling actions using transfer learning. Additionally, we have created a completely new dataset containing 8100 images of these eighteen bowlers to train the proposed framework and evaluate its performance. We have used the VGG16 model pre-trained with the ImageNet dataset and added a few layers on top of it to build our model. After trying out different strategies, we found that freezing the weights for the first 14 layers of the network and training the rest of the layers works best. Our approach achieves an overall average accuracy of 93.3% on the test set and converges to a very low cross-entropy loss.	['Siamul Karim Khan', 'Tanzil Bin Hassan', 'Md Nafee Al Islam']	2019-09-03	null	null	null	null	['game-of-cricket']	['playing-games']	[-7.81057701e-02 -2.77712792e-01 1.07838828e-02 -8.58049691e-02 -1.97084323e-01 -2.37790793e-01 4.32425946e-01 -2.85916571e-02 -8.16063941e-01 4.39483374e-01 9.41666141e-02 -2.17585228e-02 -4.39878628e-02 -1.06023872e+00 -8.33869040e-01 -5.37701786e-01 -2.13979796e-01 1.35934040e-01 6.40201747e-01 -6.51101708e-01 4.71439898e-01 5.26128039e-02 -1.57763219e+00 5.90783358e-01 3.71131152e-01 1.24158216e+00 3.11846528e-02 9.04103220e-01 1.54512689e-01 1.30835128e+00 -7.98311830e-01 -7.91677177e-01 4.88959938e-01 -3.75282735e-01 -7.90722072e-01 -4.65170473e-01 4.76405829e-01 -1.80955261e-01 -6.66651666e-01 9.27611470e-01 5.12755930e-01 3.56843323e-01 3.09807003e-01 -1.11460388e+00 -4.06942666e-01 7.01244652e-01 -5.99903405e-01 4.17071491e-01 1.08423948e-01 1.77350119e-01 9.26636815e-01 -5.27638674e-01 4.28571224e-01 9.54228580e-01 1.10030651e+00 5.52087724e-01 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-1.18509400e+00 -6.82887673e-01 5.99119008e-01 -7.13146180e-02 -9.40595746e-01 -8.22152123e-02 -5.84270917e-02]	[7.71830415725708, 0.5598528385162354]
3c96e654-6902-4093-879f-0464660bd459	learning-the-trading-algorithm-in-simulated	2208.02901	null	https://arxiv.org/abs/2208.02901v3	https://arxiv.org/pdf/2208.02901v3.pdf	Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market	We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses Bayesian optimization and a ``bandit-over-bandit'' framework to dynamically adjust strategy parameters in response to market conditions. We use Bristol Stock Exchange (BSE) to simulate financial markets containing heterogeneous populations of automated trading agents and compare PRBO with PRSH, a reference trading strategy that adapts strategy parameters through stochastic hill-climbing. Results show that PRBO generates significantly more profit than PRSH, despite having fewer hyperparameters to tune. The code for PRBO and performing experiments is available online open-source (https://github.com/HarmoniaLeo/PRZI-Bayesian-Optimisation).	['John Cartlidge', 'Bingde Liu']	2022-08-04	null	null	null	null	['bayesian-optimisation']	['methodology']	[-5.12417376e-01 -3.37212831e-01 -3.83412868e-01 -3.49735022e-02 -1.09450924e+00 -7.81277359e-01 6.11610591e-01 -4.65333313e-01 -4.01789546e-01 1.21460497e+00 4.84082885e-02 -3.63076448e-01 -5.78029931e-01 -8.00806046e-01 -7.23412335e-01 -8.99435043e-01 8.82020295e-02 1.32565320e+00 -3.42898513e-03 -1.37976408e-01 4.58955526e-01 9.84598175e-02 -9.87798274e-01 2.81007681e-02 7.23067284e-01 1.03520429e+00 -7.07018524e-02 8.15779746e-01 1.84443116e-01 5.05420208e-01 -5.45530379e-01 -4.34517652e-01 8.66146088e-01 -7.70623982e-01 -3.31748247e-01 -3.06397319e-01 -3.25335532e-01 -1.08158268e-01 2.07531571e-01 9.67982471e-01 5.85075319e-01 3.35822374e-01 5.64976156e-01 -1.00514007e+00 -4.78103071e-01 9.69680965e-01 -5.50946891e-01 6.18028879e-01 -1.11463249e-01 4.40475613e-01 1.21814585e+00 -3.04608077e-01 1.31098896e-01 1.41927183e+00 7.69590557e-01 2.52312601e-01 -1.70049632e+00 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-1.40414342e-01 2.73427993e-01 -8.43132794e-01 -5.99886239e-01 -3.72590393e-01]	[4.505102157592773, 3.320913553237915]
900f0c19-cd27-420a-861f-cb9c648ef7c6	offline-congestion-games-how-feedback-type	2210.13396	null	https://arxiv.org/abs/2210.13396v1	https://arxiv.org/pdf/2210.13396v1.pdf	Offline congestion games: How feedback type affects data coverage requirement	This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games.The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider three different types of feedback with decreasing revealed information. Starting from the facility-level (a.k.a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE. For the agent-level (a.k.a., bandit) feedback setting, interestingly, we show the one-unit deviation coverage condition is not sufficient. On the other hand, we convert the game to multi-agent linear bandits and show that with a generalized data coverage assumption in offline linear bandits, we can efficiently recover the approximate NE. Lastly, we consider a novel type of feedback, the game-level feedback where only the total reward from all agents is revealed. Again, we show the coverage assumption for the agent-level feedback setting is insufficient in the game-level feedback setting, and with a stronger version of the data coverage assumption for linear bandits, we can recover an approximate NE. Together, our results constitute the first study of offline congestion games and imply formal separations between different types of feedback.	['Simon S. Du', 'Maryam Fazel', 'Zhihan Xiong', 'Qiwen Cui', 'Haozhe Jiang']	2022-10-24	null	null	null	null	['type']	['speech']	[-0.23758298 0.56508285 -0.6715462 0.32803577 -0.6382433 -1.0691041 -0.288558 0.2610016 -0.4846608 1.1700413 -0.0207483 -0.5816445 -0.8939021 -1.1371706 -1.1194836 -0.6486151 -0.22520812 0.5242937 0.25250986 -0.30793506 -0.01054925 0.2934672 -1.037284 -0.08495475 0.9184129 0.94597274 0.14139743 1.0301017 0.02815963 0.8286075 -0.51038295 -0.438582 1.1245074 -0.48988664 -0.9742637 0.31882757 -0.08712438 -1.0011647 -0.51931053 1.1610647 0.1379335 -0.01571536 0.26894292 -1.663736 -0.30220097 1.199028 -0.6174772 0.1780993 0.16212645 0.08772802 1.295813 0.24750051 0.6731698 0.77292776 0.30722263 0.4590792 -1.3431727 -0.34887126 0.3036529 -0.25062934 -0.78886014 0.00906805 0.31981826 -0.36777002 0.52046144 0.68775445 1.0538529 0.16145502 -0.62514144 1.1302563 1.1301582 -0.30838838 0.30947396 0.13220923 0.13647021 0.39158252 0.74642736 0.22988191 -0.10777402 -0.32878295 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08040463-3539-408a-85f7-4c04993f2ba9	automatic-analysis-of-the-emotional-content	2106.09539	null	https://arxiv.org/abs/2106.09539v1	https://arxiv.org/pdf/2106.09539v1.pdf	Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit	Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of daylong recordings from preterm infants' audio environments were collected from two hospitals in Finland and Estonia in the context of so-called APPLE study. In order to analyze the emotional content of speech in such a massive dataset, an automatic speech emotion recognition (SER) system is required. However, there are no emotion labels or existing indomain SER systems to be used for this purpose. In this paper, we introduce this initially unannotated large-scale real-world audio dataset and describe the development of a functional SER system for the Finnish subset of the data. We explore the effectiveness of alternative state-of-the-art techniques to deploy a SER system to a new domain, comparing cross-corpus generalization, WGAN-based domain adaptation, and active learning in the task. As a result, we show that the best-performing models are able to achieve a classification performance of 73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification for valence and arousal, respectively. The results also show that active learning achieves the most consistent performance compared to the two alternatives.	['Okko Räsänen', 'Konstantinos Drossos', 'Sari Ahlqvist-Björkroth', 'Einari Vaaras']	2021-06-14	null	null	null	null	['cross-corpus']	['computer-vision']	[ 1.33089453e-01 3.47437978e-01 3.92789431e-02 -6.50559902e-01 -9.79236186e-01 -3.30547035e-01 8.62300172e-02 5.02497792e-01 -5.79430461e-01 5.49694121e-01 1.81632668e-01 2.27885380e-01 -2.31135160e-01 -3.60061437e-01 -5.30699611e-01 -6.41688287e-01 -1.94057807e-01 4.21281964e-01 2.33082429e-01 -7.61078149e-02 -1.01842083e-01 3.91358137e-01 -2.07345963e+00 6.77301705e-01 8.46318185e-01 1.04299974e+00 1.03448853e-01 7.00134933e-01 -3.10234666e-01 7.79535472e-01 -8.81754816e-01 -4.67300028e-01 -2.59640902e-01 -1.19016469e-01 -8.55905712e-01 -2.79959649e-01 1.53517157e-01 -1.43494874e-01 3.45661342e-01 5.52039027e-01 1.08281934e+00 2.37977892e-01 4.47428763e-01 -1.02740693e+00 -2.15742633e-01 7.37234235e-01 5.47536612e-02 2.44205579e-01 5.12570798e-01 -4.95717555e-01 7.40087211e-01 -6.99699283e-01 4.64091420e-01 7.92813718e-01 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ebceb77c-1e9b-4106-b818-e997ac0c51c9	scene-completenesss-aware-lidar-depth	2003.06945	null	https://arxiv.org/abs/2003.06945v3	https://arxiv.org/pdf/2003.06945v3.pdf	Scene Completeness-Aware Lidar Depth Completion for Driving Scenario	This paper introduces Scene Completeness-Aware Depth Completion (SCADC) to complete raw lidar scans into dense depth maps with fine and complete scene structures. Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas. These areas are considered less important since they are usually sky or trees of less scene understanding interest. However, we argue that in several driving scenarios such as large trucks or cars with loads, objects could extend to the upper parts of scenes. Thus depth maps with structured upper scene estimation are important for RGBD algorithms. SCADC adopts stereo images that produce disparities with better scene completeness but are generally less precise than lidars, to help sparse lidar depth completion. To our knowledge, we are the first to focus on scene completeness of sparse depth completion. We validate our SCADC on both depth estimate precision and scene-completeness on KITTI. Moreover, we experiment on less-explored outdoor RGBD semantic segmentation with scene completeness-aware D-input to validate our method.	['Cho-Ying Wu', 'Ulrich Neumann']	2020-03-15	null	null	null	null	['stereo-lidar-fusion']	['computer-vision']	[ 2.53188401e-01 1.73062101e-01 -4.04138863e-03 -6.47791028e-01 -5.36273718e-01 -4.82327640e-01 2.66973287e-01 2.35243991e-01 -2.92913795e-01 7.02685177e-01 -3.05381119e-01 -2.69501925e-01 1.53121175e-02 -1.23911881e+00 -6.83737218e-01 -1.97133020e-01 1.27314180e-01 1.13492227e+00 8.67379844e-01 -2.13983923e-01 1.79939643e-01 7.63696373e-01 -1.81187546e+00 5.27901165e-02 1.09069109e+00 8.88458788e-01 6.76707685e-01 5.16671479e-01 -6.95574522e-01 4.30634439e-01 -1.90864876e-01 -2.79928744e-01 6.45455658e-01 2.46157497e-02 -4.79897082e-01 2.57577628e-01 9.01768863e-01 -9.36494648e-01 -1.00488678e-01 8.94463778e-01 1.41796157e-01 -1.08549073e-01 3.94569725e-01 -1.26405263e+00 3.50518852e-01 3.14548582e-01 -7.72884488e-01 -1.38925448e-01 3.21741223e-01 2.00351000e-01 6.10648930e-01 -7.35414386e-01 6.54687166e-01 1.21687603e+00 6.35592759e-01 2.44162008e-01 -9.74782705e-01 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e5df3604-d880-49ea-8620-f1b1768a0e37	using-randomness-to-improve-robustness-of	1808.03601	null	http://arxiv.org/abs/1808.03601v1	http://arxiv.org/pdf/1808.03601v1.pdf	Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks	Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection models use and modify or remove those features to avoid detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called {\em randomization} is underexplored. This paper proposes a novel randomization-based approach to improve robustness of machine learning models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has some degree of randomness. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method. We also discuss how this approach can be applied to other ML models.	['ZhiYuan Chen', 'Fan Yang']	2018-08-10	null	null	null	null	['spam-detection']	['natural-language-processing']	[ 4.11316425e-01 -2.60798454e-01 -1.20626211e-01 -2.62067795e-01 -7.42665827e-02 -8.87426019e-01 9.38328624e-01 1.36441197e-02 -5.67128181e-01 6.55073762e-01 -3.99569005e-01 -9.84582245e-01 6.33746162e-02 -1.26090658e+00 -4.66145664e-01 -5.58892846e-01 -8.16505626e-02 6.19605243e-01 6.47420108e-01 -2.92770892e-01 7.30307937e-01 8.74071658e-01 -1.19413733e+00 4.94246781e-01 5.52593827e-01 3.05479854e-01 -2.75687128e-01 1.01566875e+00 -3.07509691e-01 7.00523019e-01 -9.34752464e-01 -3.77675951e-01 4.72021818e-01 -4.94058371e-01 -1.01942766e+00 -1.43080369e-01 1.71977878e-02 -5.38678020e-02 -1.43044710e-01 1.13315523e+00 3.86864007e-01 -1.10363536e-01 4.42233771e-01 -1.49757719e+00 -2.12663487e-01 7.79048085e-01 -5.87582231e-01 2.98254490e-01 2.91169107e-01 2.23149121e-01 2.89969653e-01 -1.60271049e-01 3.69204253e-01 1.54501784e+00 4.86485511e-01 8.14672947e-01 -1.24553645e+00 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2.35763848e-01 1.57459825e-02 -5.77594280e-01 -8.60359430e-01 -3.73885751e-01]	[5.6118388175964355, 7.620060443878174]

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