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91274cc1-8935-4086-90eb-2d30897e5007 | what-s-this-learning-to-segment-unknown | 2011.03279 | null | https://arxiv.org/abs/2011.03279v2 | https://arxiv.org/pdf/2011.03279v2.pdf | "What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences | We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed by a distinction between manipulator and object solely by observing the motion between consecutive RGB frames. In contrast to previous approaches, we propose a single, end-to-end trainable architecture which jointly incorporates motion cues and semantic knowledge. Furthermore, while the motion of the manipulator and the object are substantial cues for our algorithm, we present means to robustly deal with distraction objects moving in the background, as well as with completely static scenes. Our method neither depends on any visual registration of a kinematic robot or 3D object models, nor on precise hand-eye calibration or any additional sensor data. By extensive experimental evaluation we demonstrate the superiority of our framework and provide detailed insights on its capability of dealing with the aforementioned extreme cases of motion. We also show that training a semantic segmentation network with the automatically labeled data achieves results on par with manually annotated training data. Code and pretrained model are available at https://github.com/DLR-RM/DistinctNet. | ['Rudolph Triebel', 'Maximilian Durner', 'Martin Sundermeyer', 'Wout Boerdijk'] | 2020-11-06 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 2.61481553e-01 2.64134794e-01 -6.81123808e-02 -3.84051621e-01
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b7357523-2c0d-4442-8457-22d618d42304 | noisy-self-knowledge-distillation-for-text | 2009.07032 | null | https://arxiv.org/abs/2009.07032v2 | https://arxiv.org/pdf/2009.07032v2.pdf | Noisy Self-Knowledge Distillation for Text Summarization | In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training. Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models. We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers achieving state-of-the-art results. | ['Yang Liu', 'Mirella Lapata', 'Sheng Shen'] | 2020-09-15 | null | https://aclanthology.org/2021.naacl-main.56 | https://aclanthology.org/2021.naacl-main.56.pdf | naacl-2021-4 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 4.28186327e-01 7.79981673e-01 -4.99618351e-01 -6.24154210e-01
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162b93de-6bb8-4fa9-9e15-12e526b0d0cc | integrating-semantic-information-into-sketchy | 2301.00429 | null | https://arxiv.org/abs/2301.00429v1 | https://arxiv.org/pdf/2301.00429v1.pdf | Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension | Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results. | ['Ngan Luu-Thuy Nguyen', 'Duc-Vu Nguyen', 'Viet-Duc Ho', 'Hang Thi-Thu Le'] | 2023-01-01 | null | null | null | null | ['xlm-r', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.40313959e-01 6.12603188e-01 1.69526488e-01 -5.96285760e-01
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bdd10a04-e00c-4150-9043-7c8395381b88 | conic-challenge-pushing-the-frontiers-of | 2303.06274 | null | https://arxiv.org/abs/2303.06274v2 | https://arxiv.org/pdf/2303.06274v2.pdf | CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting | Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. | ['Nasir M. Rajpoot', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'David Snead', 'Tianyi Miao', 'Hyun Jung', 'Dorsa Ziaei', 'Jin Tae Kwak', 'Trinh Thi Le Vuong', 'Ankush Jamthikar', 'Yash Dubey', 'Vedant Phuse', 'Pranay Dumbhare', 'Satoshi Kasai', 'Satoshi Kondo', 'Taebum Lee', 'Soo-Hyung Kim', 'Vi Thi-Tuong Vo', 'Min Wu', 'Lijian Mao', 'Liukun Zhang', 'Chunhui Lin', 'Yongbing Zhang', 'Zhifan Lin', 'Zijie Fang', 'Ye Zhang', 'Jiji C V', 'Devika R G', 'Densen Puthussery', 'Hrishikesh P S', 'Jaime S. Cardoso', 'Aurélio Campilho', 'João D. Nunes', 'Pedro Costa', 'Adam Shephard', 'Raja Muhammad Saad Bashir', 'Srijay Deshpande', 'Muhammad Dawood', 'Jijun Cheng', 'Chu Han', 'Xipeng Pan', 'Zaiyi Liu', 'Hong-Kun Lin', 'Chia-Yen Lee', 'Ching-Ping Wang', 'Hsiang-Chin Chien', 'Banban Huang', 'Ralf Mikut', 'Markus Reischl', 'Marcel P. Schilling', 'Oliver Neumann', 'Muhammad Ridzuan', 'Prakash Mishra', 'Yughender Meda', 'Dhairya Talsania', 'Shuolin Liu', 'Carola-Bibiane Schönlieb', 'Dagmar Kainmueller', 'Chixin Wang', 'Weiqin Ying', 'Jiachen Li', 'Katharina Löffler', 'Moritz Böhland', 'Tim Scherr', 'Bertrand Vernay', 'Benoît Piégu', 'Marie-Claire Blache', 'Mohammad Yaqub', 'Min Xu', 'Hussam Azzuni', 'Yiyu Hong', 'Heeyoung Ahn', 'Ayushi Jain', 'Angelica I. Aviles-Rivero', 'Chenyang Hong', 'Lihao Liu', 'Peter Hirsch', 'Elias Baumann', 'Josef Lorenz Rumberger', 'Xiyue Wang', 'Jinxi Xiang', 'Sen yang', 'Jun Zhang', 'Wenhua Zhang', 'Uwe Schmidt', 'Martin Weigert', 'Mostafa Jahanifar', 'Quoc Dang Vu', 'Simon Graham'] | 2023-03-11 | null | null | null | null | ['whole-slide-images', 'nuclear-segmentation', 'survival-analysis'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 1.25414789e-01 -1.69213817e-01 -1.70947507e-01 -1.69599161e-01
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72ea15e1-0cca-4a19-88ca-f4220df5a570 | exploring-the-potential-of-sar-data-for-cloud | 2206.02850 | null | https://arxiv.org/abs/2206.02850v3 | https://arxiv.org/pdf/2206.02850v3.pdf | GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion | The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap between optical and SAR images as well as the severe speckle noise of SAR images may cause significant interference in SAR-based cloud removal, resulting in performance degeneration. In this paper, we propose a novel global-local fusion based cloud removal (GLF-CR) algorithm to leverage the complementary information embedded in SAR images. Exploiting the power of SAR information to promote cloud removal entails two aspects. The first, global fusion, guides the relationship among all local optical windows to maintain the structure of the recovered region consistent with the remaining cloud-free regions. The second, local fusion, transfers complementary information embedded in the SAR image that corresponds to cloudy areas to generate reliable texture details of the missing regions, and uses dynamic filtering to alleviate the performance degradation caused by speckle noise. Extensive evaluation demonstrates that the proposed algorithm can yield high quality cloud-free images and outperform state-of-the-art cloud removal algorithms with a gain about 1.7dB in terms of PSNR on SEN12MS-CR dataset. | ['Xiao Xiang Zhu', 'Wen Yang', 'Gui-Song Xia', 'Lei Yu', 'Patrick Ebel', 'Yilei Shi', 'Fang Xu'] | 2022-06-06 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 6.21046543e-01 -8.22700918e-01 5.58291256e-01 2.67094046e-01
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8da80e16-001a-477c-ba0c-3a796437c5b7 | monocular-camera-localization-for-automated | 2109.06296 | null | https://arxiv.org/abs/2109.06296v1 | https://arxiv.org/pdf/2109.06296v1.pdf | Monocular Camera Localization for Automated Vehicles Using Image Retrieval | We address the problem of finding the current position and heading angle of an autonomous vehicle in real-time using a single camera. Compared to methods which require LiDARs and high definition (HD) 3D maps in real-time, the proposed approach is easily scalable and computationally efficient, at the price of lower precision. The new method combines and adapts existing algorithms in three different fields: image retrieval, mapping database, and particle filtering. The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs. We evaluate the proposed method using the KITTI odometry dataset and via closed-loop experiments with an indoor 1:10 autonomous vehicle. The tests demonstrate real-time capability and a 10cm level accuracy. Also, experimental results of the closed-loop indoor tests show the presence of a positive feedback loop between the localization error and the control error. Such phenomena is analysed in details at the end of the article. | ['Francesco Borrelli', 'Eunhyek Joa'] | 2021-09-13 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.43395662e-01 -3.96723330e-01 -4.67777811e-03 -3.90061080e-01
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bb848f0a-8186-40f4-a18a-4b096fd161fa | a-multi-stage-triple-path-method-for-speech | 2303.03732 | null | https://arxiv.org/abs/2303.03732v1 | https://arxiv.org/pdf/2303.03732v1.pdf | A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments | In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized for such situations. To address this issue, we propose a multi-stage end-to-end learning method that decouples the difficult speech separation problem in noisy and reverberant environments into three sub-problems: speech denoising, separation, and de-reverberation. The probability and speed of searching for the optimal solution of the speech separation model are improved by reducing the solution space. Moreover, since the channel information of the audio sequence in the time domain is crucial for speech separation, we propose a triple-path structure capable of modeling the channel dimension of audio sequences. Experimental results show that the proposed multi-stage triple-path method can improve the performance of speech separation models at the cost of little model parameter increment. | ['Wenjing Zhu', 'Xiangyuan Yang', 'Xinyu Yang', 'Zhaoxi Mu'] | 2023-03-07 | null | null | null | null | ['speech-separation', 'speech-denoising'] | ['speech', 'speech'] | [-5.88339195e-02 -3.87969196e-01 3.53386402e-01 -1.69190571e-01
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79bd6993-1c46-4d6d-a53f-9a4d97da5fe6 | exploiting-pairwise-mutual-information-for | null | null | https://ieeexplore.ieee.org/abstract/document/9739959 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9739959 | Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue | External document knowledge is helpful for dialogue systems to generate high-quality responses. Although several knowledge-grounded dialogue models have been designed, external knowledge cannot be comprehensively exploited due to the complex relationships among dialogue context, knowledge, and responses. To this end, we propose a novel transformer-based model, named TransIKG, which incorporates external document knowledge for dialogue generation. TransIKG comprises a two-step integration mechanism, including correlation integration and overall integration. Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information. Furthermore, we utilize the positional information of dialogue turns to better represent the dialogue context and enhance the generalization ability of our model on out-of-domain documents. Finally, we propose a novel knowledge-aware pointer network to generate knowledge-enhanced response tokens. Experimental results on two benchmark datasets demonstrate that our model outperforms state-of-the-art models on both open-domain and domain-specific dialogues. | ['Bo Xu', 'Hui Ma', 'Hongfei Lin', 'Jian Wang', 'Bo Zhang'] | 2022-03-22 | null | null | null | ieee-acm-transactions-on-audio-speech-and-8 | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 4.75312807e-02 1.33985594e-01 -2.35055774e-01 -4.49773759e-01
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2c7348ec-19e1-4d80-9da4-f91ac15d73c8 | detailed-annotations-of-chest-x-rays-via-ct | 2210.03416 | null | https://arxiv.org/abs/2210.03416v1 | https://arxiv.org/pdf/2210.03416v1.pdf | Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding | In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner structures of a patient. As such, formulating reports requires medical experts to possess wide-ranging knowledge about anatomical regions with their normal, healthy appearance as well as the ability to recognize abnormalities. This explicit grasp on both the patient's anatomy and their appearance is missing in current medical image-processing systems as annotations are especially difficult to gather. This renders the models to be narrow experts e.g. for identifying specific diseases. In this work, we recover this missing link by adding human anatomy into the mix and enable the association of content in medical reports to their occurrence in associated imagery (medical phrase grounding). To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data. Our evaluation shows that methods that take advantage of anatomical information benefit heavily in visually grounding radiologists' findings, as our anatomical segmentations allow for up to absolute 50% better grounding results on the OpenI dataset as compared to commonly used region proposals. The PAXRay dataset is available at https://constantinseibold.github.io/paxray/. | ['Rainer Stiefelhagen', 'Jens Kleesiek', 'Klaus H. Maier-Hein', 'Moon Sung Kim', 'Jan Sellner', 'Victoria Mayer', 'Matthias A. Fink', 'Saquib Sarfraz', 'Simon Reiß', 'Constantin Seibold'] | 2022-10-07 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.69350475e-01 7.33954549e-01 -2.16972485e-01 -3.62394869e-01
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03d55ce0-bbe9-485b-bc6b-122b7f36eceb | exploring-plausible-patches-using-source-code | 2103.16846 | null | https://arxiv.org/abs/2103.16846v1 | https://arxiv.org/pdf/2103.16846v1.pdf | Exploring Plausible Patches Using Source Code Embeddings in JavaScript | Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite. Although (re-) running the test suite is right under one's nose, in real life applications the problem of over- and underfitting often occurs, resulting in inadequate patches. Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more. Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches. Our goal is to investigate the nature of these similarity-based approaches. To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification - it fails to capture nuanced code semantics. Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair. | ['László Vidács', 'Márk Lajkó', 'Dániel Horváth', 'Viktor Csuvik'] | 2021-03-31 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 9.52354372e-02 -3.32275480e-02 3.10265124e-02 -2.69444376e-01
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bf470881-fd1c-4836-a966-c205069bd668 | cornet-a-neurosymbolic-approach-to-learning | 2208.06032 | null | https://arxiv.org/abs/2208.06032v4 | https://arxiv.org/pdf/2208.06032v4.pdf | CORNET: Learning Table Formatting Rules By Example | Spreadsheets are widely used for table manipulation and presentation. Stylistic formatting of these tables is an important property for both presentation and analysis. As a result, popular spreadsheet software, such as Excel, supports automatically formatting tables based on rules. Unfortunately, writing such formatting rules can be challenging for users as it requires knowledge of the underlying rule language and data logic. We present CORNET, a system that tackles the novel problem of automatically learning such formatting rules from user examples in the form of formatted cells. CORNET takes inspiration from advances in inductive programming and combines symbolic rule enumeration with a neural ranker to learn conditional formatting rules. To motivate and evaluate our approach, we extracted tables with over 450K unique formatting rules from a corpus of over 1.8M real worksheets. Since we are the first to introduce conditional formatting, we compare CORNET to a wide range of symbolic and neural baselines adapted from related domains. Our results show that CORNET accurately learns rules across varying evaluation setups. Additionally, we show that CORNET finds shorter rules than those that a user has written and discovers rules in spreadsheets that users have manually formatted. | ['Gust Verbruggen', 'Mohammad Raza', 'Carina Negreanu', 'Vu Le', 'Sumit Gulwani', 'José Cambronero', 'Mukul Singh'] | 2022-08-11 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.65511632e-01 9.93156433e-02 -2.21405908e-01 -6.18920922e-01
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6e267809-7207-4362-a812-a87aa4ba856e | a-large-scale-event-based-detection-dataset | 2001.08499 | null | https://arxiv.org/abs/2001.08499v3 | https://arxiv.org/pdf/2001.08499v3.pdf | A Large Scale Event-based Detection Dataset for Automotive | We introduce the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304x240 ATIS sensor. It contains open roads and very diverse driving scenarios, ranging from urban, highway, suburbs and countryside scenes, as well as different weather and illumination conditions. Manual bounding box annotations of cars and pedestrians contained in the recordings are also provided at a frequency between 1 and 4Hz, yielding more than 255,000 labels in total. We believe that the availability of a labeled dataset of this size will contribute to major advances in event-based vision tasks such as object detection and classification. We also expect benefits in other tasks such as optical flow, structure from motion and tracking, where for example, the large amount of data can be leveraged by self-supervised learning methods. | ['Pierre de Tournemire', 'Davide Nitti', 'Etienne Perot', 'Davide Migliore', 'Amos Sironi'] | 2020-01-23 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [-2.27694176e-02 -1.34569496e-01 -1.27341971e-01 -5.67265689e-01
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58d844b5-b7cd-4fd7-ac06-a53dcabcb8bf | multimodal-generalized-zero-shot-learning-for | 2111.07646 | null | https://arxiv.org/abs/2111.07646v1 | https://arxiv.org/pdf/2111.07646v1.pdf | Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning | Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non-interventional and easily acquired. We solve the problem in a generalized zero-shot learning (GZSL) setting since we may not access training images of every disease grade. Synthetic MRI feature vectors of unseen grades (classes) are generated by exploiting Gleason grades' ordered nature through a conditional variational autoencoder (CVAE) incorporating self-supervised learning. Corresponding histopathology features are generated using cycle GANs, and combined with MR features to predict Gleason grades of test images. Experimental results show our method outperforms competing feature generating approaches for GZSL, and comes close to performance of fully supervised methods. | ['Dwarikanath Mahapatra'] | 2021-11-15 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 8.18433285e-01 7.08154678e-01 -3.88908803e-01 -7.15073287e-01
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68cf1f4d-6b16-4670-a2fa-5d54415b8a10 | hinerv-video-compression-with-hierarchical | 2306.09818 | null | https://arxiv.org/abs/2306.09818v1 | https://arxiv.org/pdf/2306.09818v1.pdf | HiNeRV: Video Compression with Hierarchical Encoding based Neural Representation | Learning-based video compression is currently one of the most popular research topics, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods. However, existing INR-based methods have failed to deliver rate quality performance comparable with the state of the art in video compression. This is mainly due to the simplicity of the employed network architectures, which limit their representation capability. In this paper, we propose HiNeRV, an INR that combines bilinear interpolation with novel hierarchical positional encoding. This structure employs depth-wise convolutional and MLP layers to build a deep and wide network architecture with much higher capacity. We further build a video codec based on HiNeRV and a refined pipeline for training, pruning and quantization that can better preserve HiNeRV's performance during lossy model compression. The proposed method has been evaluated on both UVG and MCL-JCV datasets for video compression, demonstrating significant improvement over all existing INRs baselines and competitive performance when compared to learning-based codecs (72.3% overall bit rate saving over HNeRV and 43.4% over DCVC on the UVG dataset, measured in PSNR). | ['David Bull', 'Andrew Gower', 'Fan Zhang', 'Ge Gao', 'Ho Man Kwan'] | 2023-06-16 | null | null | null | null | ['video-compression', 'quantization', 'model-compression'] | ['computer-vision', 'methodology', 'methodology'] | [ 2.72082806e-01 -1.04976475e-01 -4.47684526e-01 -2.09350705e-01
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2c90863d-e2e9-4075-bc0a-c83f68eda845 | tridentadapt-learning-domain-invariance-via | 2111.15300 | null | https://arxiv.org/abs/2111.15300v2 | https://arxiv.org/pdf/2111.15300v2.pdf | TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation | Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domain adaptation perspective, the key challenge is to learn domain-agnostic representation of the inputs in order to benefit from virtual data. In this paper, we propose a novel trident-like architecture that enforces a shared feature encoder to satisfy confrontational source and target constraints simultaneously, thus learning a domain-invariant feature space. Moreover, we also introduce a novel training pipeline enabling self-induced cross-domain data augmentation during the forward pass. This contributes to a further reduction of the domain gap. Combined with a self-training process, we obtain state-of-the-art results on benchmark datasets (e.g. GTA5 or Synthia to Cityscapes adaptation). Code and pre-trained models are available at https://github.com/HMRC-AEL/TridentAdapt | ['Alois Knoll', 'Onay Urfalioglu', 'Ahmet Faruk Tuna', 'Akhil Gurram', 'Fengyi Shen'] | 2021-11-30 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.77779400e-01 1.71310142e-01 -2.54072368e-01 -7.25143492e-01
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0c50e0e8-2c25-4180-a0b6-523b710933c8 | deep-residual-networks-for-automatic-sleep | 1810.03745 | null | http://arxiv.org/abs/1810.03745v1 | http://arxiv.org/pdf/1810.03745v1.pdf | Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms | We have developed an automatic sleep stage classification algorithm based on
deep residual neural networks and raw polysomnogram signals. Briefly, the raw
data is passed through 50 convolutional layers before subsequent classification
into one of five sleep stages. Three model configurations were trained on 1850
polysomnogram recordings and subsequently tested on 230 independent recordings.
Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of
0.746, improving on previous reported results by other groups also using only
raw polysomnogram data. Most errors were made on non-REM stage 1 and 3
decisions, errors likely resulting from the definition of these stages. Further
testing on independent cohorts is needed to verify performance for clinical
use. | ['Alexander Neergaard Olesen', 'Poul Jennum', 'Helge Bjarup Dissing Sorensen', 'Emmanuel Mignot', 'Paul Peppard'] | 2018-10-08 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [ 1.04040027e-01 7.41962530e-03 4.79350891e-03 -6.93624496e-01
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7ad671fa-49e2-42cb-8a15-9ba4d001a625 | context-aware-multi-task-learning-for-traffic | 2004.01351 | null | https://arxiv.org/abs/2004.01351v1 | https://arxiv.org/pdf/2004.01351v1.pdf | Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles | Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task-specific set of features for all possible tasks of inference-time, which ignores the capability to leverage common task-invariant contextual knowledge for the task at hand. To address this problem, we propose an algorithm to jointly learn the task-specific and shared representations by adopting a multi-task learning network. Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly. The learned representations capture richer contextual information without additional task-specific network. Extensive experiments on the large-scale dataset HSD demonstrate the effectiveness and superiority of our network over state-of-the-art methods. | ['Younkwan Lee', 'Moongu Jeon', 'Jihyo Jeon', 'Jongmin Yu'] | 2020-04-03 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.27009380e-01 -3.10368627e-01 -3.71276468e-01 -6.77065134e-01
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d762c6b5-8959-44b0-a68a-61fb74702bff | unsupervised-pretraining-for-object-detection | 2103.04814 | null | https://arxiv.org/abs/2103.04814v2 | https://arxiv.org/pdf/2103.04814v2.pdf | Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors | Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning global representations for image-level classification tasks instead of discriminative local region representations, which limits their transferability to region-level downstream tasks, such as object detection. To improve the transferability of pre-trained features to object detection, we present Deeply Unsupervised Patch Re-ID (DUPR), a simple yet effective method for unsupervised visual representation learning. The patch Re-ID task treats individual patch as a pseudo-identity and contrastively learns its correspondence in two views, enabling us to obtain discriminative local features for object detection. Then the proposed patch Re-ID is performed in a deeply unsupervised manner, appealing to object detection, which usually requires multilevel feature maps. Extensive experiments demonstrate that DUPR outperforms state-of-the-art unsupervised pre-trainings and even the ImageNet supervised pre-training on various downstream tasks related to object detection. | ['Gui-Song Xia', 'Ping Luo', 'Zhenguo Li', 'Chenhan Jiang', 'Hang Xu', 'Enze Xie', 'Jian Ding'] | 2021-03-08 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.80595452e-01 7.74673298e-02 -5.51129937e-01 -2.81211674e-01
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1d8b31d7-0560-474e-819e-4d674d7ba162 | beyond-negativity-re-analysis-and-follow-up | 2306.01742 | null | https://arxiv.org/abs/2306.01742v1 | https://arxiv.org/pdf/2306.01742v1.pdf | Beyond Negativity: Re-Analysis and Follow-Up Experiments on Hope Speech Detection | Health experts assert that hope plays a crucial role in enhancing individuals' physical and mental well-being, facilitating their recovery, and promoting restoration. Hope speech refers to comments, posts and other social media messages that offer support, reassurance, suggestions, inspiration, and insight. The detection of hope speech involves the analysis of such textual content, with the aim of identifying messages that invoke positive emotions in people. Our study aims to find computationally efficient yet comparable/superior methods for hope speech detection. We also make our codebase public at https://github.com/aflah02/Hope_Speech_Detection | ['Raghav Sahni', 'Diksha Sethi', 'Mohammad Aflah Khan', 'Neemesh Yadav'] | 2023-05-10 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.51683557e-01 3.24904203e-01 -7.89710701e-01 -1.52663857e-01
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86e586b8-d2ea-49ef-ac6d-383afd259587 | hierarchical-sparse-subspace-clustering-hessc | null | null | https://www.mdpi.com/2072-4292/12/15/2421 | https://www.mdpi.com/2072-4292/12/15/2421/htm | Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis | Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms. | ['and Richard Gloaguen', 'Raimon Tolosana-Delgado', 'Pedram Ghamisi', 'Laura Tusa', 'Mahdi Khodadadzadeh', 'Kasra Rafiezadeh Shahi'] | 2020-07-28 | null | null | null | null | ['sparse-subspace-based-clustering'] | ['computer-code'] | [ 2.30633542e-01 -6.69651210e-01 7.75788128e-02 -7.61028454e-02
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864c83c1-c951-4d0a-bc81-840f8dea6f11 | self-annotated-training-for-controllable | 2110.08446 | null | https://arxiv.org/abs/2110.08446v2 | https://arxiv.org/pdf/2110.08446v2.pdf | Self-Annotated Training for Controllable Image Captioning | The Controllable Image Captioning (CIC) task aims to generate captions conditioned on designated control signals. Several structure-related control signals are proposed to control the semantic structure of sentences, such as sentence length and Part-of-Speech tag sequences. However, due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures, existing reinforcement training methods are not applicable to structure-related CIC models. The lack of reinforcement training leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics. In this paper, we propose a novel reinforcement training method for structure-related control signals: Self-Annotated Training (SAT), to improve both the accuracy and controllability of CIC models. In SAT, a recursive annotation mechanism (RAM) is designed to force the input control signal to match the actual output sentence. Moreover, we propose an extra alignment reward to finetune the CIC model trained after SAT method, which further enhances the controllability of models. On the MSCOCO benchmark, we conduct extensive experiments on different structure-related control signals and on different baseline models, the results of which demonstrate the effectiveness and generalizability of our methods. | ['Hong Qu', 'Tianlei Wang', 'Zhangzi Zhu'] | 2021-10-16 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 6.15538239e-01 2.27298632e-01 -2.29514301e-01 -6.82261586e-01
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37851a24-d923-4dc9-9d7e-0b55b8bf3273 | latent-state-marginalization-as-a-low-cost | 2210.00999 | null | https://arxiv.org/abs/2210.00999v2 | https://arxiv.org/pdf/2210.00999v2.pdf | Latent State Marginalization as a Low-cost Approach for Improving Exploration | While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work, we propose the adoption of latent variable policies within the MaxEnt framework, which we show can provably approximate any policy distribution, and additionally, naturally emerges under the use of world models with a latent belief state. We discuss why latent variable policies are difficult to train, how naive approaches can fail, then subsequently introduce a series of improvements centered around low-cost marginalization of the latent state, allowing us to make full use of the latent state at minimal additional cost. We instantiate our method under the actor-critic framework, marginalizing both the actor and critic. The resulting algorithm, referred to as Stochastic Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training. Our implementation is open sourced at https://github.com/zdhNarsil/Stochastic-Marginal-Actor-Critic. | ['Ricky T. Q. Chen', 'Amy Zhang', 'Qinqing Zheng', 'Yoshua Bengio', 'Aaron Courville', 'Dinghuai Zhang'] | 2022-10-03 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.19800413e-01 3.70063633e-01 -5.30859292e-01 1.06143780e-01
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75a96551-e459-4aae-bb13-e23f4ff61944 | activemocap-optimized-drone-flight-for-active | 1912.08568 | null | https://arxiv.org/abs/1912.08568v2 | https://arxiv.org/pdf/1912.08568v2.pdf | ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture | The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the problem that we address in this paper. Specifically, given a short video sequence, we introduce an algorithm that predicts which viewpoints should be chosen to capture future frames so as to maximize 3D human pose estimation accuracy. The key idea underlying our approach is a method to estimate the uncertainty of the 3D body pose estimates. We integrate several sources of uncertainty, originating from deep learning based regressors and temporal smoothness. Our motion planner yields improved 3D body pose estimates and outperforms or matches existing ones that are based on person following and orbiting. | ['Pascal Fua', 'Sena Kiciroglu', 'Mathieu Salzmann', 'Sudipta N. Sinha', 'Helge Rhodin'] | 2019-12-18 | activemocap-optimized-viewpoint-selection-for | http://openaccess.thecvf.com/content_CVPR_2020/html/Kiciroglu_ActiveMoCap_Optimized_Viewpoint_Selection_for_Active_Human_Motion_Capture_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kiciroglu_ActiveMoCap_Optimized_Viewpoint_Selection_for_Active_Human_Motion_Capture_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-8.93837214e-02 8.91349614e-02 -2.89066821e-01 -2.52069384e-01
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5f0b2695-02ef-4314-96f2-de19418c84d1 | the-kitmus-test-evaluating-knowledge | 2212.08192 | null | https://arxiv.org/abs/2212.08192v2 | https://arxiv.org/pdf/2212.08192v2.pdf | The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems | Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution subtasks that require reasoning over multiple facts. These subtasks differ in terms of which knowledge sources contain the relevant facts. We also introduce subtasks where knowledge is present only at inference time using fictional knowledge. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources. Still, even the best performing models seem to have difficulties with reliably integrating knowledge presented only at inference time. | ['Jackie Chi Kit Cheung', 'Alexandra Olteanu', 'Adam Trischler', 'Kaheer Suleman', 'Martin Pömsl', 'Akshatha Arodi'] | 2022-12-15 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.42556155e-01 6.48585856e-01 -4.93807286e-01 -3.83267552e-01
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eaa40d70-7405-46f0-b738-feb7536fcfe5 | cortical-microcircuits-as-gated-recurrent | 1711.02448 | null | http://arxiv.org/abs/1711.02448v2 | http://arxiv.org/pdf/1711.02448v2.pdf | Cortical microcircuits as gated-recurrent neural networks | Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term memory networks (LSTMs), we introduce a recurrent neural
network in which information is gated through inhibitory cells that are
subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known
canonical excitatory-inhibitory cortical microcircuits. Our empirical
evaluation across sequential image classification and language modelling tasks
shows that subLSTM units can achieve similar performance to LSTM units. These
results suggest that cortical circuits can be optimised to solve complex
contextual problems and proposes a novel view on their computational function.
Overall our work provides a step towards unifying recurrent networks as used in
machine learning with their biological counterparts. | ['Brendan Shillingford', 'Tim P. Vogels', 'Nando de Freitas', 'Yannis M. Assael', 'Rui Ponte Costa'] | 2017-11-07 | cortical-microcircuits-as-gated-recurrent-1 | http://papers.nips.cc/paper/6631-cortical-microcircuits-as-gated-recurrent-neural-networks | http://papers.nips.cc/paper/6631-cortical-microcircuits-as-gated-recurrent-neural-networks.pdf | neurips-2017-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 5.65724313e-01 2.56408215e-01 1.86140582e-01 -9.42121819e-02
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b0d882c8-787d-4db4-8e02-773160f281e8 | thuir2-at-ntcir-16-session-search-ss-task | 2307.00250 | null | https://arxiv.org/abs/2307.00250v1 | https://arxiv.org/pdf/2307.00250v1.pdf | THUIR2 at NTCIR-16 Session Search (SS) Task | Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task. This paper describes our approaches and results. In the FOSS subtask, we submit five runs using learning-to-rank and fine-tuned pre-trained language models. We fine-tuned the pre-trained language model with ad-hoc data and session information and assembled them by a learning-to-rank method. The assembled model achieves the best performance among all participants in the preliminary evaluation. In the POSS subtask, we used an assembled model which also achieves the best performance in the preliminary evaluation. | ['Shaoping Ma', 'Min Zhang', 'Yiqun Liu', 'Xiangsheng Li', 'Weihang Su'] | 2023-07-01 | null | null | null | null | ['learning-to-rank', 'learning-to-rank', 'session-search'] | ['graphs', 'miscellaneous', 'natural-language-processing'] | [-4.69602346e-01 -7.00741932e-02 -1.26972422e-01 -6.25519216e-01
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482a3fd5-31a7-45b3-8bc7-44aaf41316e3 | neural-morphological-tagging-from-characters | 1606.06640 | null | http://arxiv.org/abs/1606.06640v1 | http://arxiv.org/pdf/1606.06640v1.pdf | Neural Morphological Tagging from Characters for Morphologically Rich Languages | This paper investigates neural character-based morphological tagging for
languages with complex morphology and large tag sets. We systematically explore
a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain
character-based word vectors combined with bidirectional LSTMs to model
across-word context in an end-to-end setting. We explore supplementary use of
word-based vectors trained on large amounts of unlabeled data. Our experiments
for morphological tagging suggest that for "simple" model configurations, the
choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or
the augmentation with pre-trained word embeddings can be important and clearly
impact the accuracy. Increasing the model capacity by adding depth, for
example, and carefully optimizing the neural networks can lead to substantial
improvements, and the differences in accuracy (but not training time) become
much smaller or even negligible. Overall, our best morphological taggers for
German and Czech outperform the best results reported in the literature by a
large margin. | ['Guenter Neumann', 'Josef van Genabith', 'Georg Heigold'] | 2016-06-21 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [ 6.25220463e-02 -1.65431965e-02 -1.39094535e-02 -2.21884191e-01
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1b0647a2-6fb3-4bff-97d8-b1769406fac1 | asr-is-all-you-need-cross-modal-distillation | 1911.12747 | null | https://arxiv.org/abs/1911.12747v2 | https://arxiv.org/pdf/1911.12747v2.pdf | ASR is all you need: cross-modal distillation for lip reading | The goal of this work is to train strong models for visual speech recognition without requiring human annotated ground truth data. We achieve this by distilling from an Automatic Speech Recognition (ASR) model that has been trained on a large-scale audio-only corpus. We use a cross-modal distillation method that combines Connectionist Temporal Classification (CTC) with a frame-wise cross-entropy loss. Our contributions are fourfold: (i) we show that ground truth transcriptions are not necessary to train a lip reading system; (ii) we show how arbitrary amounts of unlabelled video data can be leveraged to improve performance; (iii) we demonstrate that distillation significantly speeds up training; and, (iv) we obtain state-of-the-art results on the challenging LRS2 and LRS3 datasets for training only on publicly available data. | ['Triantafyllos Afouras', 'Joon Son Chung', 'Andrew Zisserman'] | 2019-11-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 5.49301088e-01 2.65322626e-01 -4.20187950e-01 -2.92486995e-01
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29909c88-e20c-4014-91c7-8d1029cb6191 | framework-and-benchmarks-for-combinatorial | 2306.09803 | null | https://arxiv.org/abs/2306.09803v1 | https://arxiv.org/pdf/2306.09803v1.pdf | Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization | This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field. Current MCBO papers often introduce non-diverse or non-standard benchmarks to evaluate their methods, impeding the proper assessment of different MCBO primitives and their combinations. Additionally, papers introducing a solution for a single MCBO primitive often omit benchmarking against baselines that utilize the same methods for the remaining primitives. This omission is primarily due to the significant implementation overhead involved, resulting in a lack of controlled assessments and an inability to showcase the merits of a contribution effectively. To overcome these challenges, our proposed framework enables an effortless combination of Bayesian Optimization components, and provides a diverse set of synthetic and real-world benchmarking tasks. Leveraging this flexibility, we implement 47 novel MCBO algorithms and benchmark them against seven existing MCBO solvers and five standard black-box optimization algorithms on ten tasks, conducting over 4000 experiments. Our findings reveal a superior combination of MCBO primitives outperforming existing approaches and illustrate the significance of model fit and the use of a trust region. We make our MCBO library available under the MIT license at \url{https://github.com/huawei-noah/HEBO/tree/master/MCBO}. | ['Haitham Bou Ammar', 'Antoine Grosnit', 'Kamil Dreczkowski'] | 2023-06-16 | null | null | null | null | ['bayesian-optimization', 'benchmarking', 'benchmarking'] | ['methodology', 'miscellaneous', 'robots'] | [-3.89690958e-02 -2.79947549e-01 -3.77778947e-01 -3.58039439e-01
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476223cf-94f2-4dc3-be04-7d4def2515bc | traffic-sign-detection-and-recognition-for | 1911.05626 | null | https://arxiv.org/abs/1911.05626v1 | https://arxiv.org/pdf/1911.05626v1.pdf | Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment | This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs; and using more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed extremely well under good illumination and weather conditions. Its drawbacks are that it sometimes failed to detect object under bad weather conditions like snow and failed to distinguish speed limits signs with different limit values. | ['Ziyuan Pu', 'Zhiyong Cui', 'Meixin Zhu', 'Yinhai Wang', 'Liangwu Yan', 'Jingyun Hu'] | 2019-10-27 | null | null | null | null | ['traffic-sign-detection', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [-1.82585754e-02 -4.50277001e-01 2.50509620e-01 -2.93297082e-01
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45c756f8-2cc9-4e67-83aa-45d5f51ff404 | sampling-from-discrete-energy-based-models-1 | 2112.05702 | null | https://arxiv.org/abs/2112.05702v1 | https://arxiv.org/pdf/2112.05702v1.pdf | Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs | Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. However, they do not provide a mechanism for obtaining exact samples from these distributions. Monte Carlo techniques can aid us in obtaining samples if some proposal distribution that we can easily sample from is available. For instance, rejection sampling can provide exact samples but is often difficult or impossible to apply due to the need to find a proposal distribution that upper-bounds the target distribution everywhere. Approximate Markov chain Monte Carlo sampling techniques like Metropolis-Hastings are usually easier to design, exploiting a local proposal distribution that performs local edits on an evolving sample. However, these techniques can be inefficient due to the local nature of the proposal distribution and do not provide an estimate of the quality of their samples. In this work, we propose a new approximate sampling technique, Quasi Rejection Sampling (QRS), that allows for a trade-off between sampling efficiency and sampling quality, while providing explicit convergence bounds and diagnostics. QRS capitalizes on the availability of high-quality global proposal distributions obtained from deep learning models. We demonstrate the effectiveness of QRS sampling for discrete EBMs over text for the tasks of controlled text generation with distributional constraints and paraphrase generation. We show that we can sample from such EBMs with arbitrary precision at the cost of sampling efficiency. | ['Marc Dymetman', 'Hady Elsahar', 'Germán Kruszewski', 'Bryan Eikema'] | 2021-12-10 | sampling-from-discrete-energy-based-models | https://openreview.net/forum?id=9zcjXdavnX | https://openreview.net/pdf?id=9zcjXdavnX | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [-1.18108010e-02 -2.79366851e-01 -3.46087873e-01 -2.76997626e-01
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0afe2e7b-90d1-4c1d-8add-b05e184a49bb | learning-by-grouping-a-multilevel | 2304.00486 | null | https://arxiv.org/abs/2304.00486v1 | https://arxiv.org/pdf/2304.00486v1.pdf | Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy | The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent advancements in this field. However, it has been discovered that there is a tradeoff between the accuracy and fairness of these decision-making tasks. In some cases, these AI systems can be unfair by exhibiting bias or discrimination against certain social groups, which can have severe consequences in real life. Inspired by one of the most well-known human learning skills called grouping, we address this issue by proposing a novel machine learning framework where the ML model learns to group a diverse set of problems into distinct subgroups to solve each subgroup using its specific sub-model. Our proposed framework involves three stages of learning, which are formulated as a three-level optimization problem: (i) learning to group problems into different subgroups; (ii) learning group-specific sub-models for problem-solving; and (iii) updating group assignments of training examples by minimizing the validation loss. These three learning stages are performed end-to-end in a joint manner using gradient descent. To improve fairness and accuracy, we develop an efficient optimization algorithm to solve this three-level optimization problem. To further reduce the risk of overfitting in small datasets, we incorporate domain adaptation techniques in the second stage of training. We further apply our method to neural architecture search. Extensive experiments on various datasets demonstrate our method's effectiveness and performance improvements in both fairness and accuracy. Our proposed Learning by Grouping can reduce overfitting and achieve state-of-the-art performances with fixed human-designed network architectures and searchable network architectures on various datasets. | ['Pengtao Xie', 'Bhanu Garg', 'Li Zhang', 'Ramtin Hosseini'] | 2023-04-02 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.80682588e-01 -6.17845468e-02 -4.39770550e-01 -7.07680225e-01
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a163b9a2-86c0-4158-87c3-8b4a2e68180e | foreground-segmentation-using-a-triplet | 1801.02225 | null | http://arxiv.org/abs/1801.02225v1 | http://arxiv.org/pdf/1801.02225v1.pdf | Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding | A common approach for moving objects segmentation in a scene is to perform a
background subtraction. Several methods have been proposed in this domain.
However, they lack the ability of handling various difficult scenarios such as
illumination changes, background or camera motion, camouflage effect, shadow
etc. To address these issues, we propose a robust and flexible encoder-decoder
type neural network based approach. We adapt a pre-trained convolutional
network, i.e. VGG-16 Net, under a triplet framework in the encoder part to
embed an image in multiple scales into the feature space and use a transposed
convolutional network in the decoder part to learn a mapping from feature space
to image space. We train this network end-to-end by using only a few training
samples. Our network takes an RGB image in three different scales and produces
a foreground segmentation probability mask for the corresponding image. In
order to evaluate our model, we entered the Change Detection 2014 Challenge
(changedetection.net) and our method outperformed all the existing
state-of-the-art methods by an average F-Measure of 0.9770. Our source code
will be made publicly available at https://github.com/lim-anggun/FgSegNet. | ['Long Ang Lim', 'Hacer Yalim Keles'] | 2018-01-07 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.21238768e-01 -4.08292592e-01 3.48357111e-01 -3.93873215e-01
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40b80587-b541-40a7-b475-8d8ee7298a20 | a-thousand-frames-in-just-a-few-words-lingual | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Das_A_Thousand_Frames_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Das_A_Thousand_Frames_2013_CVPR_paper.pdf | A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching | The problem of describing images through natural language has gained importance in the computer vision community. Solutions to image description have either focused on a top-down approach of generating language through combinations of object detections and language models or bottom-up propagation of keyword tags from training images to test images through probabilistic or nearest neighbor techniques. In contrast, describing videos with natural language is a less studied problem. In this paper, we combine ideas from the bottom-up and top-down approaches to image description and propose a method for video description that captures the most relevant contents of a video in a natural language description. We propose a hybrid system consisting of a low level multimodal latent topic model for initial keyword annotation, a middle level of concept detectors and a high level module to produce final lingual descriptions. We compare the results of our system to human descriptions in both short and long forms on two datasets, and demonstrate that final system output has greater agreement with the human descriptions than any single level. | ['Jason J. Corso', 'Pradipto Das', 'Chenliang Xu', 'Richard F. Doell'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['video-description'] | ['computer-vision'] | [ 1.96927190e-01 1.70513481e-01 -2.14196146e-01 -7.31842518e-01
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877a5139-dd23-4fcc-b36a-c9fde04dcf69 | feature-learning-with-gaussian-restricted | 1309.6176 | null | http://arxiv.org/abs/1309.6176v1 | http://arxiv.org/pdf/1309.6176v1.pdf | Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition | In this paper, we first present a new variant of Gaussian restricted
Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann
machine (MGRBM), with its definition and learning algorithm. Then we propose
using a learned GRBM or MGRBM to extract better features for robust speech
recognition. Our experiments on Aurora2 show that both GRBM-extracted and
MGRBM-extracted feature performs much better than Mel-frequency cepstral
coefficient (MFCC) with either HMM-GMM or hybrid HMM-deep neural network (DNN)
acoustic model, and MGRBM-extracted feature is slightly better. | ['Helen Meng', 'Xin Zheng', 'Zhiyong Wu', 'Weifeng Li', 'Lianhong Cai'] | 2013-09-23 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [-1.57181308e-01 -9.75401402e-02 -1.64063275e-01 -6.04516387e-01
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589773a7-2d55-4923-913e-03a4bee7d855 | semeval-2019-task-9-suggestion-mining-from | null | null | https://aclanthology.org/S19-2151 | https://aclanthology.org/S19-2151.pdf | SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums | We present the pilot SemEval task on Suggestion Mining. The task consists of subtasks A and B, where we created labeled data from feedback forum and hotel reviews respectively. Subtask A provides training and test data from the same domain, while Subtask B evaluates the system on a test dataset from a different domain than the available training data. 33 teams participated in the shared task, with a total of 50 members. We summarize the problem definition, benchmark dataset preparation, and methods used by the participating teams, providing details of the methods used by the top ranked systems. The dataset is made freely available to help advance the research in suggestion mining, and reproduce the systems submitted under this task | ['Sapna Negi', 'Paul Buitelaar', 'Tobias Daudert'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.09045357e-01 3.04131031e-01 -2.94273257e-01 -6.91980898e-01
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258a1c83-96c7-48c9-9d68-85b39d283dde | a-review-of-audio-features-and-statistical | 1502.06811 | null | http://arxiv.org/abs/1502.06811v1 | http://arxiv.org/pdf/1502.06811v1.pdf | A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design | Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed. | ['Hien-Thanh Duong', 'Ngoc Q. K. Duong'] | 2015-02-24 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 5.83884537e-01 -5.87100327e-01 -2.35517889e-01 -2.32623518e-01
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9cafe45f-4b85-49f8-87a1-81b1f85c1798 | efficient-bilateral-cross-modality-cluster | 2305.12673 | null | https://arxiv.org/abs/2305.12673v2 | https://arxiv.org/pdf/2305.12673v2.pdf | Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID | Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships between cross-modality clusters are not well explored. To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Specifically, we design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM) algorithm through optimizing the maximum matching problem in a bipartite graph. Then, the matched pairwise clusters utilize shared visible and infrared pseudo-labels during the model training. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Meanwhile, the cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the large modality discrepancy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art approaches by a large margin of 8.76% mAP on average. | ['Xinbo Gao', 'Zhen Wang', 'Shizhou Zhang', 'Nannan Wang', 'Lingfeng He', 'De Cheng'] | 2023-05-22 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 1.79740742e-01 -2.50491679e-01 -4.20828938e-01 -6.26882434e-01
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189e2191-5e24-4a57-bed1-de4aec87466a | emotion-recognition-in-conversations-with | 1910.04980 | null | https://arxiv.org/abs/1910.04980v3 | https://arxiv.org/pdf/1910.04980v3.pdf | Conversational Transfer Learning for Emotion Recognition | Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annotated data, which is lacking in most of the available corpora in this task. To tackle this challenge, we look at transfer learning approaches as a viable alternative. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context. We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target). In addition to the popular practice of using pre-trained sentence encoders, our approach also incorporates recurrent parameters that model inter-sentential context across the whole conversation. Based on this idea, we perform several experiments across multiple datasets and find improvement in performance and robustness against limited training data. TL-ERC also achieves better validation performances in significantly fewer epochs. Overall, we infer that knowledge acquired from dialogue generators can indeed help recognize emotions in conversations. | ['Roger Zimmermann', 'Soujanya Poria', 'Devamanyu Hazarika', 'Rada Mihalcea'] | 2019-10-11 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.56852150e-01 4.00328726e-01 9.52560231e-02 -7.27971852e-01
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13be2c1f-fc7d-46ba-824f-3fc162256da4 | detection-and-attention-diagnosing-pulmonary | 1712.05114 | null | http://arxiv.org/abs/1712.05114v1 | http://arxiv.org/pdf/1712.05114v1.pdf | Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians | This paper proposes a novel and efficient method to build a Computer-Aided
Diagnoses (CAD) system for lung nodule detection based on Computed Tomography
(CT). This task was treated as an Object Detection on Video (VID) problem by
imitating how a radiologist reads CT scans. A lung nodule detector was trained
to automatically learn nodule features from still images to detect lung nodule
candidates with both high recall and accuracy. Unlike previous work which used
3-dimensional information around the nodule to reduce false positives, we
propose two simple but efficient methods, Multi-slice propagation (MSP) and
Motionless-guide suppression (MLGS), which analyze sequence information of CT
scans to reduce false negatives and suppress false positives. We evaluated our
method in open-source LUNA16 dataset which contains 888 CT scans, and obtained
state-of-the-art result (Free-Response Receiver Operating Characteristic score
of 0.892) with detection speed (end to end within 20 seconds per patient on a
single NVidia GTX 1080) much higher than existing methods. | ['Wei Guo', 'Ning Li', 'Kungang Li', 'Shijun Zhao', 'Jie He', 'Haopeng Liu', 'Bin Qiu'] | 2017-12-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.81490141e-01 5.47179654e-02 -4.48952258e-01 -1.86379790e-01
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03cccde2-1af0-4c6b-8caa-b1aab360b73d | from-representation-to-reasoning-towards-both | 2205.14895 | null | https://arxiv.org/abs/2205.14895v1 | https://arxiv.org/pdf/2205.14895v1.pdf | From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering | Video understanding has achieved great success in representation learning, such as video caption, video object grounding, and video descriptive question-answer. However, current methods still struggle on video reasoning, including evidence reasoning and commonsense reasoning. To facilitate deeper video understanding towards video reasoning, we present the task of Causal-VidQA, which includes four types of questions ranging from scene description (description) to evidence reasoning (explanation) and commonsense reasoning (prediction and counterfactual). For commonsense reasoning, we set up a two-step solution by answering the question and providing a proper reason. Through extensive experiments on existing VideoQA methods, we find that the state-of-the-art methods are strong in descriptions but weak in reasoning. We hope that Causal-VidQA can guide the research of video understanding from representation learning to deeper reasoning. The dataset and related resources are available at \url{https://github.com/bcmi/Causal-VidQA.git}. | ['Liqing Zhang', 'Li Niu', 'Jiangtong Li'] | 2022-05-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-question-answering'] | ['computer-vision'] | [ 2.10993662e-01 3.10710847e-01 -6.54743016e-01 -4.69720691e-01
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58dfaeca-e9bb-487d-aea8-382b0da0c7af | diameter-estimation-of-cylindrical-metal-bar | 2212.13021 | null | https://arxiv.org/abs/2212.13021v1 | https://arxiv.org/pdf/2212.13021v1.pdf | Diameter Estimation of Cylindrical Metal Bar Using Wideband Dual-Polarized Ground-Penetrating Radar | Ground-penetrating radar (GPR) has been an effective technology for locating metal bars in civil engineering structures. However, the accurate sizing of subsurface metal bars of small diameters remains a challenging problem for the existing reflection pattern-based method due to the limited resolution of GPR. To address the issue, we propose a reflection power-based method by exploring the relationship between the bar diameter and the maximum power of the bar reflected signal obtained by a wideband dual-polarized GPR, which circumvents the resolution limit of the existing pattern-based method. In the proposed method, the theoretical relationship between the bar diameter and the power ratio of the bar reflected signals acquired by perpendicular and parallel polarized antennas is established via the inherent scattering width of the metal bar and the wideband spectrum of the bar reflected signal. Based on the theoretical relationship, the bar diameter can be estimated using the obtained power ratio in a GPR survey. Simulations and experiments have been conducted with different GPR frequency spectra, subsurface mediums, and metal bars of various diameters and depths to demonstrate the efficacy of the method. Experimental results show that the method achieves high sizing accuracy with errors of less than 10% in different scenarios. With its simple operation and high accuracy, the method can be implemented in real-time in situ examination of subsurface metal bars. | ['Zheng Fan', 'Weixia Cheng', 'Hai-Han Sun'] | 2022-12-26 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.34968528e-01 6.24447092e-02 8.13715756e-01 2.40647405e-01
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adef0ea9-655e-4c44-9ad0-6f680ce6f799 | hypertime-hyperparameter-optimization-for | 2305.18421 | null | https://arxiv.org/abs/2305.18421v1 | https://arxiv.org/pdf/2305.18421v1.pdf | HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts | In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based on this observation, we leverage the `worst-case-oriented' philosophy from the robust optimization literature to help find such robust hyperparameter configurations. HyperTime imposes a lexicographic priority order on average validation loss and worst-case validation loss over chronological validation sets. We perform a theoretical analysis on the upper bound of the expected test loss, which reveals the unique advantages of our approach. We also demonstrate the strong empirical performance of the proposed method on multiple machine learning tasks with temporal distribution shifts. | ['Chi Wang', 'Qingyun Wu', 'Zhonghua Zheng', 'Yiran Wu', 'Shaokun Zhang'] | 2023-05-28 | null | null | null | null | ['hyperparameter-optimization', 'philosophy'] | ['methodology', 'miscellaneous'] | [ 8.26757103e-02 -1.56601816e-01 -2.69451052e-01 -2.36805633e-01
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5e70f914-1b24-4c32-b25c-1be7c6451508 | semantic-neural-model-approach-for-face | 2305.01058 | null | https://arxiv.org/abs/2305.01058v1 | https://arxiv.org/pdf/2305.01058v1.pdf | semantic neural model approach for face recognition from sketch | Face sketch synthesis and reputation have wide range of packages in law enforcement. Despite the amazing progresses had been made in faces cartoon and reputation, maximum current researches regard them as separate responsibilities. On this paper, we propose a semantic neural version approach so that you can address face caricature synthesis and recognition concurrently. We anticipate that faces to be studied are in a frontal pose, with regular lighting and neutral expression, and have no occlusions. To synthesize caricature/image photos, the face vicinity is divided into overlapping patches for gaining knowledge of. The size of the patches decides the scale of local face systems to be found out. | ['Raghupathi Reddy Allapuram', 'Sandhya Jukanti', 'Chandana Navuluri'] | 2023-05-01 | null | null | null | null | ['face-recognition', 'face-sketch-synthesis', 'caricature'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.7370472e-03 1.9181396e-01 -9.9014409e-02 -5.7162058e-01
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1cd0f194-c3e2-4032-814f-2e8b2b9b35a5 | unsupervised-few-shot-learning-via-self | null | null | https://openreview.net/forum?id=Ske-ih4FPS | https://openreview.net/pdf?id=Ske-ih4FPS | Unsupervised Few Shot Learning via Self-supervised Training | Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In the current study, we develop a method to learn an unsupervised few-shot learner via self-supervised training (UFLST), which can effectively generalize to novel but related classes. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate with each other, and eventually produce a high quality few-shot learner. Using the benchmark dataset Omniglot, we show that our model outperforms other unsupervised few-shot learning methods to a large extend and approaches to the performances of supervised methods. Using the benchmark dataset Market1501, we further demonstrate the feasibility of our model to a real-world application on person re-identification. | ['Si Wu', 'Tiejun Huang', 'Xiaolong Zou', 'Zilong Ji'] | 2019-09-25 | null | null | null | null | ['unsupervised-few-shot-learning'] | ['computer-vision'] | [ 2.71736592e-01 -4.64437902e-02 -2.84428000e-01 -5.13996959e-01
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202d3676-b15c-459b-aa31-f56ec3f48f55 | few-shot-named-entity-recognition-with | null | null | https://openreview.net/forum?id=5aeBigqvO-P | https://openreview.net/pdf?id=5aeBigqvO-P | Few-Shot Named Entity Recognition with Biaffine Span Representation | While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data (i.e., few-shot NER) has been challenging. Correspondingly, the N-way K-shot NER task is proposed to recognize entities in the given N categories with only K labeled samples for each category. Existing methods treat this task as a sequence labeling problem, while this paper regards it as an entity span classification problem and designs a Biaffine Span Representation (BSR) method to learn contextual span dependency representation to fit into the classification algorithm. The BSR applies a biaffine pooling module to establish the dependencies of each word on the whole sentence and to reduce the dimension of word features, thus, the span representation could gain contextual dependency information to help improve recognition accuracies. Experimental study on four standard NER datasets shows that our proposed BSR method outperforms pre-trained language models and existing N-way K-shot NER algorithms in two types of adaptations (i.e., Intra-Domain Cross-Type Adaptation and Cross-Domain Cross-Type Adaptation). Notably, F_1 value has increased by an average of 13.77% and 18.30% on the 5-way 1-shot task and the 5-way 5-shot task, respectively. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-ner'] | ['natural-language-processing'] | [-2.39554700e-02 -1.25144482e-01 -1.05910301e-01 -6.37578785e-01
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9983c5aa-4d35-4203-ac77-30b6ceb40724 | using-fictitious-class-representations-to | 2111.13550 | null | https://arxiv.org/abs/2111.13550v1 | https://arxiv.org/pdf/2111.13550v1.pdf | Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners | Focusing on discriminative zero-shot learning, in this work we introduce a novel mechanism that dynamically augments during training the set of seen classes to produce additional fictitious classes. These fictitious classes diminish the model's tendency to fixate during training on attribute correlations that appear in the training set but will not appear in newly exposed classes. The proposed model is tested within the two formulations of the zero-shot learning framework; namely, generalized zero-shot learning (GZSL) and classical zero-shot learning (CZSL). Our model improves the state-of-the-art performance on the CUB dataset and reaches comparable results on the other common datasets, AWA2 and SUN. We investigate the strengths and weaknesses of our method, including the effects of catastrophic forgetting when training an end-to-end zero-shot model. | ['Ran El-Yaniv', 'Mohammed Dabbah'] | 2021-11-26 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.51566625e-01 1.90646991e-01 -1.34928659e-01 -2.94524163e-01
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74f3bd8b-438c-47f5-8c1e-64b3a0b776db | timematch-unsupervised-cross-region | 2111.02682 | null | https://arxiv.org/abs/2111.02682v3 | https://arxiv.org/pdf/2111.02682v3.pdf | TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation | The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from Satellite Image Time Series (SITS). However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions. Although various unsupervised domain adaptation techniques have been proposed in recent years, no method explicitly learns the temporal shift of SITS and thus provides only limited benefits for crop classification. To address this, we propose TimeMatch, which explicitly accounts for the temporal shift for improved SITS-based domain adaptation. In TimeMatch, we first estimate the temporal shift from the target to the source region using the predictions of a source-trained model. Then, we re-train the model for the target region by an iterative algorithm where the estimated shift is used to generate accurate target pseudo-labels. Additionally, we introduce an open-access dataset for cross-region adaptation from SITS in four different regions in Europe. On our dataset, we demonstrate that TimeMatch outperforms all competing methods by 11% in average F1-score across five different adaptation scenarios, setting a new state-of-the-art in cross-region adaptation. | ['Ira Assent', 'Sébastien Lefèvre', 'Charlotte Pelletier', 'Joachim Nyborg'] | 2021-11-04 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 3.32712978e-01 -5.63767254e-01 -5.54896295e-01 -4.83384728e-01
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ae9d108f-1ef1-43f5-a5c4-19154f620845 | planercnn-3d-plane-detection-and | 1812.04072 | null | http://arxiv.org/abs/1812.04072v2 | http://arxiv.org/pdf/1812.04072v2.pdf | PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image | This paper proposes a deep neural architecture, PlaneRCNN, that detects and
reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN
employs a variant of Mask R-CNN to detect planes with their plane parameters
and segmentation masks. PlaneRCNN then jointly refines all the segmentation
masks with a novel loss enforcing the consistency with a nearby view during
training. The paper also presents a new benchmark with more fine-grained plane
segmentations in the ground-truth, in which, PlaneRCNN outperforms existing
state-of-the-art methods with significant margins in the plane detection,
segmentation, and reconstruction metrics. PlaneRCNN makes an important step
towards robust plane extraction, which would have an immediate impact on a wide
range of applications including Robotics, Augmented Reality, and Virtual
Reality. | ['Yasutaka Furukawa', 'Jinwei Gu', 'Chen Liu', 'Jan Kautz', 'Kihwan Kim'] | 2018-12-10 | planercnn-3d-plane-detection-and-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_PlaneRCNN_3D_Plane_Detection_and_Reconstruction_From_a_Single_Image_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_PlaneRCNN_3D_Plane_Detection_and_Reconstruction_From_a_Single_Image_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-plane-detection'] | ['computer-vision'] | [ 4.17223155e-01 3.95821571e-01 7.95049295e-02 -4.51696038e-01
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13d40601-2463-432f-b60e-17e55772f5d5 | construction-of-semantic-collocation-bank | null | null | https://aclanthology.org/Y15-2027 | https://aclanthology.org/Y15-2027.pdf | Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing | null | ['Yu Ding', 'Yanqiu Shao', 'Shijun Liu', 'Lijuan Zheng'] | 2015-10-01 | construction-of-semantic-collocation-bank-1 | https://aclanthology.org/Y15-2027 | https://aclanthology.org/Y15-2027.pdf | paclic-2015-10 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7b9d5e5d-e3ff-448e-9bc8-a7a773bad01a | deep-attention-based-classification-network | 1807.03959 | null | http://arxiv.org/abs/1807.03959v1 | http://arxiv.org/pdf/1807.03959v1.pdf | Deep attention-based classification network for robust depth prediction | In this paper, we present our deep attention-based classification (DABC)
network for robust single image depth prediction, in the context of the Robust
Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our
goal is to design a model that can perform well in both indoor and outdoor
scenes with a single parameter set. However, robust depth prediction suffers
from two challenging problems: a) How to extract more discriminative features
for different scenes (compared to a single scene)? b) How to handle the large
differences of depth ranges between indoor and outdoor datasets? To address
these two problems, we first formulate depth prediction as a multi-class
classification task and apply a softmax classifier to classify the depth label
of each pixel. We then introduce a global pooling layer and a channel-wise
attention mechanism to adaptively select the discriminative channels of
features and to update the original features by assigning important channels
with higher weights. Further, to reduce the influence of quantization errors,
we employ a soft-weighted sum inference strategy for the final prediction.
Experimental results on both indoor and outdoor datasets demonstrate the
effectiveness of our method. It is worth mentioning that we won the 2-nd place
in single image depth prediction entry of ROB 2018, in conjunction with IEEE
Conference on Computer Vision and Pattern Recognition (CVPR) 2018. | ['Hao Lu', 'Chunhua Shen', 'Zhiguo Cao', 'Ke Xian', 'Ruibo Li', 'Lingxiao Hang'] | 2018-07-11 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.07198989e-01 -7.08331168e-02 -1.21627763e-01 -6.79228663e-01
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af53387a-6c4d-4679-bfc2-222365b53ab1 | multi-hop-reading-comprehension-via-deep | 1905.09438 | null | https://arxiv.org/abs/1905.09438v1 | https://arxiv.org/pdf/1905.09438v1.pdf | Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal | Reading Comprehension has received significant attention in recent years as high quality Question Answering (QA) datasets have become available. Despite state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH) reasoning remains particularly challenging. To address MH-QA specifically, we propose a Deep Reinforcement Learning based method capable of learning sequential reasoning across large collections of documents so as to pass a query-aware, fixed-size context subset to existing models for answer extraction. Our method is comprised of two stages: a linker, which decomposes the provided support documents into a graph of sentences, and an extractor, which learns where to look based on the current question and already-visited sentences. The result of the linker is a novel graph structure at the sentence level that preserves logical flow while still allowing rapid movement between documents. Importantly, we demonstrate that the sparsity of the resultant graph is invariant to context size. This translates to fewer decisions required from the Deep-RL trained extractor, allowing the system to scale effectively to large collections of documents. The importance of sequential decision making in the document traversal step is demonstrated by comparison to standard IE methods, and we additionally introduce a BM25-based IR baseline that retrieves documents relevant to the query only. We examine the integration of our method with existing models on the recently proposed QAngaroo benchmark and achieve consistent increases in accuracy across the board, as well as a 2-3x reduction in training time. | ['Alex Long', 'Alan Blair', 'Joel Mason', 'Wei Wang'] | 2019-05-23 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 4.72746670e-01 3.52006823e-01 -1.89756319e-01 -4.35863376e-01
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67d63293-36c5-48e9-99a2-9a59e199e6b8 | clusterlog-clustering-logs-for-effective-log | 2301.07846 | null | https://arxiv.org/abs/2301.07846v1 | https://arxiv.org/pdf/2301.07846v1.pdf | ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection | With the increasing prevalence of scalable file systems in the context of High Performance Computing (HPC), the importance of accurate anomaly detection on runtime logs is increasing. But as it currently stands, many state-of-the-art methods for log-based anomaly detection, such as DeepLog, have encountered numerous challenges when applied to logs from many parallel file systems (PFSes), often due to their irregularity and ambiguity in time-based log sequences. To circumvent these problems, this study proposes ClusterLog, a log pre-processing method that clusters the temporal sequence of log keys based on their semantic similarity. By grouping semantically and sentimentally similar logs, this approach aims to represent log sequences with the smallest amount of unique log keys, intending to improve the ability of a downstream sequence-based model to effectively learn the log patterns. The preliminary results of ClusterLog indicate not only its effectiveness in reducing the granularity of log sequences without the loss of important sequence information but also its generalizability to different file systems' logs. | ['Di Zhang', 'Dong Dai', 'Chris Egersdoerfer'] | 2023-01-19 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [-6.89142719e-02 -6.04036748e-01 3.32276195e-01 -3.30011159e-01
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74f37962-9c25-4488-ad8b-499f534c6ac0 | understanding-health-misinformation | 2101.01076 | null | https://arxiv.org/abs/2101.01076v7 | https://arxiv.org/pdf/2101.01076v7.pdf | Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning | Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility. | ['Xiao Liu', 'Jiaheng Xie'] | 2020-12-21 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-5.37040271e-02 6.26193047e-01 -1.24957871e+00 -4.77750182e-01
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66d3299b-61a3-4197-9e24-91454c631854 | real-time-visual-tracking-promoting-the | 1608.08173 | null | http://arxiv.org/abs/1608.08173v2 | http://arxiv.org/pdf/1608.08173v2.pdf | Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning | Correlation filtering based tracking model has received lots of attention and
achieved great success in real-time tracking, however, the lost function in
current correlation filtering paradigm could not reliably response to the
appearance changes caused by occlusion and illumination variations. This study
intends to promote the robustness of the correlation filter learning. By
exploiting the anisotropy of the filter response, three sparsity related loss
functions are proposed to alleviate the overfitting issue of previous methods
and improve the overall tracking performance. As a result, three real-time
trackers are implemented. Extensive experiments in various challenging
situations demonstrate that the robustness of the learned correlation filter
has been greatly improved via the designed loss functions. In addition, the
study reveals, from an experimental perspective, how different loss functions
essentially influence the tracking performance. An important conclusion is that
the sensitivity of the peak values of the filter in successive frames is
consistent with the tracking performance. This is a useful reference criterion
in designing a robust correlation filter for visual tracking. | ['Ziming Zhang', 'Yao Sui', 'Li Zhang', 'Yafei Tang', 'Guanghui Wang'] | 2016-08-29 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-2.52164006e-01 -6.44405901e-01 -2.01555729e-01 -9.69104543e-02
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44d6c1f8-f4ad-4b01-beed-1e7d6cb02d38 | neural-chinese-word-segmentation-as-sequence | 1911.12982 | null | https://arxiv.org/abs/1911.12982v1 | https://arxiv.org/pdf/1911.12982v1.pdf | Neural Chinese Word Segmentation as Sequence to Sequence Translation | Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct models based on local features rather than considering global information of input sequence. In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework. The model captures the global information from the input and directly outputs the segmented sequence. It can also tackle other NLP tasks with CWS jointly in an end-to-end mode. Experiments on Weibo, PKU and MSRA benchmark datasets show that our approach has achieved competitive performances compared with state-of-the-art methods. Meanwhile, we successfully applied our proposed model to jointly learning CWS and Chinese spelling correction, which demonstrates its applicability of multi-task fusion. | ['He-Yan Huang', 'Yi-Kun Tang', 'Yuhang Guo', 'Ping Jian', 'Xuewen Shi', 'Xiaochi Wei'] | 2019-11-29 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 8.45824718e-01 -3.17321092e-01 -2.89966136e-01 -5.43840408e-01
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6eb96052-e018-4c89-b59f-53b5f6b2010e | a-deep-learning-framework-for-traffic-data | 2304.09182 | null | https://arxiv.org/abs/2304.09182v1 | https://arxiv.org/pdf/2304.09182v1.pdf | A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies | Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time. Despite the vast amount of useful information, the ST data usually suffer from the issue of missing or incomplete data, which also limits its applications. Imputation is one viable solution and is often used to prepossess the data for further applications. However, in practice, n practice, spatiotemporal data imputation is quite difficult due to the complexity of spatiotemporal dependencies with dynamic changes in the traffic network and is a crucial prepossessing task for further applications. Existing approaches mostly only capture the temporal dependencies in time series or static spatial dependencies. They fail to directly model the spatiotemporal dependencies, and the representation ability of the models is relatively limited. | ['Chan', 'Wai Kin', 'George P. Chan', 'Chenyu Tian', 'Qiruyi Zuo', 'Ting Zhang', 'Li Jiang'] | 2023-04-18 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 4.47221771e-02 -8.45166683e-01 -4.59071428e-01 -2.78636664e-01
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e03c180c-1ec3-4bd1-af41-92a411b02356 | dual-distribution-discrepancy-for-anomaly | 2206.03935 | null | https://arxiv.org/abs/2206.03935v3 | https://arxiv.org/pdf/2206.03935v3.pdf | Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays | Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an unsupervised fashion is very promising. However, almost all of the existing methods consider anomaly detection as a one-class classification (OCC) problem. They model the distribution of only known normal images during training and identify the samples not conforming to normal profile as anomalies in the testing phase. A large number of unlabeled images containing anomalies are thus ignored in the training phase, although they are easy to obtain in clinical practice. In this paper, we propose a novel strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images and unlabeled images. The proposed method consists of two modules. During training, one module takes both known normal and unlabeled images as inputs, capturing anomalous features from unlabeled images in some way, while the other one models the distribution of only known normal images. Subsequently, inter-discrepancy between the two modules, and intra-discrepancy inside the module that is trained on only normal images are designed as anomaly scores to indicate anomalies. Experiments on three CXR datasets demonstrate that the proposed DDAD achieves consistent, significant gains and outperforms state-of-the-art methods. Code is available at https://github.com/caiyu6666/DDAD. | ['Kwang-Ting Cheng', 'Yu Zhou', 'Xin Yang', 'Hao Chen', 'Yu Cai'] | 2022-06-08 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.09969482e-01 -2.53754139e-01 -1.81015551e-01 -3.70382965e-01
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21b58f15-71fc-4c95-9fd6-08452410739d | an-equivalent-graph-reconstruction-model-and | 2307.02183 | null | https://arxiv.org/abs/2307.02183v1 | https://arxiv.org/pdf/2307.02183v1.pdf | An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction | Recommendation algorithm plays an important role in recommendation system (RS), which predicts users' interests and preferences for some given items based on their known information. Recently, a recommendation algorithm based on the graph Laplacian regularization was proposed, which treats the prediction problem of the recommendation system as a reconstruction issue of small samples of the graph signal under the same graph model. Such a technique takes into account both known and unknown labeled samples information, thereby obtaining good prediction accuracy. However, when the data size is large, solving the reconstruction model is computationally expensive even with an approximate strategy. In this paper, we propose an equivalent reconstruction model that can be solved exactly with extremely low computational cost. Finally, a final prediction algorithm is proposed. We find in the experiments that the proposed method significantly reduces the computational cost while maintaining a good prediction accuracy. | ['Zhihua Yang', 'Qing Zhang', 'Lihua Yang', 'Guangrui Yang'] | 2023-07-05 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-4.27372660e-03 -6.87198043e-02 -4.65857625e-01 -4.45277691e-01
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3d87c2dc-3d0d-403e-8ade-c91cc3b38990 | zenseact-open-dataset-a-large-scale-and | 2305.02008 | null | https://arxiv.org/abs/2305.02008v1 | https://arxiv.org/pdf/2305.02008v1.pdf | Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving | Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9x that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. The dataset is accompanied by an extensive development kit. Data and more information are available online (https://zod.zenseact.com). | ['Christoffer Petersson', 'Jenny Widahl', 'Junsheng Fu', 'Daria Motorniuk', 'Carl Lindstrom', 'Adam Lilja', 'Georg Hess', 'Adam Tonderski', 'William Ljungbergh', 'Mina Alibeigi'] | 2023-05-03 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.79511055e-01 -1.65051535e-01 -3.13801169e-01 -7.55658448e-01
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e99d8ece-68da-43cf-82af-d9955db46d2a | transformer-based-global-3d-hand-pose | 2210.11384 | null | https://arxiv.org/abs/2210.11384v1 | https://arxiv.org/pdf/2210.11384v1.pdf | Transformer-based Global 3D Hand Pose Estimation in Two Hands Manipulating Objects Scenarios | This report describes our 1st place solution to ECCV 2022 challenge on Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras (hand pose estimation). In this challenge, we aim to estimate global 3D hand poses from the input image where two hands and an object are interacting on the egocentric viewpoint. Our proposed method performs end-to-end multi-hand pose estimation via transformer architecture. In particular, our method robustly estimates hand poses in a scenario where two hands interact. Additionally, we propose an algorithm that considers hand scales to robustly estimate the absolute depth. The proposed algorithm works well even when the hand sizes are various for each person. Our method attains 14.4 mm (left) and 15.9 mm (right) errors for each hand in the test set. | ['Seungryul Baek', 'Seongyeong Lee', 'Chanwoo Kim', 'Donguk Kim', 'Hoseong Cho'] | 2022-10-20 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-1.94189280e-01 -9.38980058e-02 1.96741849e-01 -2.59500220e-02
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ebbf39d7-9d8b-4439-b7b7-3005647fccda | combining-scatter-transform-and-deep-neural | 2010.07639 | null | https://arxiv.org/abs/2010.07639v1 | https://arxiv.org/pdf/2010.07639v1.pdf | Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification | An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features apparent on heart rate scales. For this reason we incorporate a variant of the complex wavelet transform, called a scatter transform, in a deep residual neural network (ResNet). The former has the advantage of being derived from theory, making it well behaved under certain transformations of the input. The latter has proven useful in ECG classification, allowing feature extraction and classification to be learned in an end-to-end manner. Through the incorporation of trainable layers in between scatter transforms, the model gains the ability to combine information from different channels, yielding more informative features for the classification task and adapting them to the specific domain. For evaluation, we submitted our model in the official phase in the PhysioNet/Computing in Cardiology Challenge 2020. Our (Team Triage) approach achieved a challenge validation score of 0.640, and full test score of 0.485, placing us 4th out of 41 in the official ranking. | ['Jan Steffan', 'Felix P Kemeth', 'Maximilian Riehl', 'Maximilian P Oppelt'] | 2020-10-15 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.76566565e-01 -7.41873458e-02 2.23607793e-01 -5.09302974e-01
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d9d8a4a2-f4f4-41ef-b93f-7f20326839d5 | chain-of-thought-prompting-for-responding-to | 2305.11792 | null | https://arxiv.org/abs/2305.11792v1 | https://arxiv.org/pdf/2305.11792v1.pdf | Chain-of-thought prompting for responding to in-depth dialogue questions with LLM | The way and content in which users ask questions can provide insight into their current status, including their personality, emotions, and psychology. Instead of directly prompting the large language models (LLMs), we explore how chain-of-thought prompting helps in this scenario to perform reasoning and planning according to user status, aiming to provide a more personalized and engaging experience for the user query. To this end, we first construct a benchmark of 6 dialogue or question-answering datasets in both English and Chinese, covering 3 different aspects of user status (\textit{including} \textit{personality}, \textit{emotion}, and \textit{psychology}). Then we prompt the LLMs to generate the response regarding the user status as intermediate reasoning processing. We propose a novel demonstration selection strategy using the semantic similarity of intermediate reasoning instead of test queries. To evaluate the effectiveness and robustness of our approach, we conduct extensive experiments with 7 LLMs under zero-shot and one-shot settings. The experimental results show that our approach consistently outperforms standard prompting in terms of both \textit{helpfulness} and \textit{acceptness} across all datasets, regardless of the LLMs used. The code and dataset can be found at \url{https://github.com/ruleGreen/Dialogue\_CoT.git}. | ['Kam-Fai Wong', 'Ruifeng Xu', 'Zezhong Wang', 'Fei Mi', 'Rui Wang', 'Hongru Wang'] | 2023-05-19 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-9.50364172e-02 3.98186892e-01 -5.52378334e-02 -6.60233676e-01
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fb2cd3bb-aea3-437e-a604-01f23c86c2b7 | a-survey-on-deep-domain-adaptation-and-tiny | 2107.07927 | null | https://arxiv.org/abs/2107.07927v1 | https://arxiv.org/pdf/2107.07927v1.pdf | A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets | This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. We mainly highlighted object detection by three different trending strategies, i.e., 1) domain adaptive deep learning-based approaches (discrepancy-based, Adversarial-based, Reconstruction-based, Hybrid). We examined general as well as tiny object detection-related challenges and offered solutions by historical and comparative analysis. In part 2) we mainly focused on tiny object detection techniques (multi-scale feature learning, Data augmentation, Training strategy (TS), Context-based detection, GAN-based detection). In part 3), To obtain knowledge-able findings, we discussed different object detection methods, i.e., convolutions and convolutional neural networks (CNN), pooling operations with trending types. Furthermore, we explained results with the help of some object detection algorithms, i.e., R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, which are generally considered the base bone of CV, CNN, and OD. We performed comparative analysis on different datasets such as MS-COCO, PASCAL VOC07,12, and ImageNet to analyze results and present findings. At the end, we showed future directions with existing challenges of the field. In the future, OD methods and models can be analyzed for real-time object detection, tracking strategies. | ['Xi Li', 'Muhammed Muzammul'] | 2021-07-16 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-4.71940562e-02 -2.26524055e-01 2.30727032e-01 1.01608291e-01
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e8d4c912-ac2b-458f-9a29-87e300ac73c5 | characterizing-the-emotion-carriers-of-covid | 2306.13954 | null | https://arxiv.org/abs/2306.13954v1 | https://arxiv.org/pdf/2306.13954v1.pdf | Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States | The COVID-19 Infodemic had an unprecedented impact on health behaviors and outcomes at a global scale. While many studies have focused on a qualitative and quantitative understanding of misinformation, including sentiment analysis, there is a gap in understanding the emotion-carriers of misinformation and their differences across geographies. In this study, we characterized emotion carriers and their impact on vaccination rates in India and the United States. A manually labelled dataset was created from 2.3 million tweets and collated with three publicly available datasets (CoAID, AntiVax, CMU) to train deep learning models for misinformation classification. Misinformation labelled tweets were further analyzed for behavioral aspects by leveraging Plutchik Transformers to determine the emotion for each tweet. Time series analysis was conducted to study the impact of misinformation on spatial and temporal characteristics. Further, categorical classification was performed using transformer models to assign categories for the misinformation tweets. Word2Vec+BiLSTM was the best model for misinformation classification, with an F1-score of 0.92. The US had the highest proportion of misinformation tweets (58.02%), followed by the UK (10.38%) and India (7.33%). Disgust, anticipation, and anger were associated with an increased prevalence of misinformation tweets. Disgust was the predominant emotion associated with misinformation tweets in the US, while anticipation was the predominant emotion in India. For India, the misinformation rate exhibited a lead relationship with vaccination, while in the US it lagged behind vaccination. Our study deciphered that emotions acted as differential carriers of misinformation across geography and time. These carriers can be monitored to develop strategic interventions for countering misinformation, leading to improved public health. | ['Tavpritesh Sethi', 'Akshaya Devadiga', 'Sargun Nagpal', 'Gopal Mengi', 'Kriti Agrawal', 'Deepak Mahto', 'Sanjana S', 'Ridam Pal'] | 2023-06-24 | null | null | null | null | ['misinformation', 'sentiment-analysis', 'time-series'] | ['miscellaneous', 'natural-language-processing', 'time-series'] | [-3.40017974e-01 -2.34106898e-01 -4.96941417e-01 -8.60369578e-02
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80988545-1650-4ded-aa1f-0e53fca22ac1 | utilizing-every-image-object-for-semi | 2011.02655 | null | https://arxiv.org/abs/2011.02655v1 | https://arxiv.org/pdf/2011.02655v1.pdf | Utilizing Every Image Object for Semi-supervised Phrase Grounding | Phrase grounding models localize an object in the image given a referring expression. The annotated language queries available during training are limited, which also limits the variations of language combinations that a model can see during training. In this paper, we study the case applying objects without labeled queries for training the semi-supervised phrase grounding. We propose to use learned location and subject embedding predictors (LSEP) to generate the corresponding language embeddings for objects lacking annotated queries in the training set. With the assistance of the detector, we also apply LSEP to train a grounding model on images without any annotation. We evaluate our method based on MAttNet on three public datasets: RefCOCO, RefCOCO+, and RefCOCOg. We show that our predictors allow the grounding system to learn from the objects without labeled queries and improve accuracy by 34.9\% relatively with the detection results. | ['Ram Nevatia', 'Zhaoheng Zheng', 'Arka Sadhu', 'Haidong Zhu'] | 2020-11-05 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.08289444e-01 5.82872748e-01 -3.90600085e-01 -3.81721169e-01
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62a24118-9f27-453d-bf23-d2bb8d3af675 | content-based-brain-tumor-retrieval-for-mr | null | null | https://ieeexplore.ieee.org/document/8611216 | https://doi.org/10.1109/ACCESS.2019.2892455 | Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning | This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors
on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR
systems for MR images is the semantic gap between the low-level visual information captured by the MRI
machine and the high-level information perceived by the human evaluator. The traditional feature extraction
methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap.
It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features
by encoding/combining low-level and high-level features. Deep learning is very powerful for feature
representation that can depict low-level and high-level information completely and embed the phase of feature
extraction in self-learning. Therefore, we propose a deep convolutional neural network (CNN) VGG19-based
novel feature extraction framework and apply closed-form metric learning (CFML) to measure the similarity
between the query image and database images. Furthermore, we adopt transfer learning and propose a blockwise fine-tuning strategy to enhance the retrieval performance. Extensive experiments are performed on a
publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and
pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal
views. Our method is more generic, as we do not use any handcrafted features; it requires minimal
preprocessing, tested as robust on five-fold cross-validation, can achieve a five-fold mean average precision
(mAP) of 96.13% and outperforms state-of-the-art CBIR systems on the CE-MRI dataset. | ['Jianfeng Lu', 'Saeed Ahmad', 'Ali Zakir', 'Farman Ali', 'Muhammad Kabir', 'Qinghua Zhao3', 'Zar Nawab Khan Swati'] | 2019-01-04 | null | null | null | journal-2019-1 | ['content-based-image-retrieval', 'self-learning'] | ['computer-vision', 'natural-language-processing'] | [ 1.32755592e-01 -3.79116088e-01 -1.55671328e-01 -4.87848967e-01
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ea6bd3ce-f046-4349-8725-8a78070b5b06 | learning-transformation-aware-embeddings-for | 2001.04547 | null | https://arxiv.org/abs/2001.04547v1 | https://arxiv.org/pdf/2001.04547v1.pdf | Learning Transformation-Aware Embeddings for Image Forensics | A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporated increased usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper addresses Image Provenance Analysis, which aims at discovering relationships among different manipulated image versions that share content. One of the main sub-problems for provenance analysis that has not yet been addressed directly is the edit ordering of images that share full content or are near-duplicates. The existing large networks that generate image descriptors for tasks such as object recognition may not encode the subtle differences between these image covariates. This paper introduces a novel deep learning-based approach to provide a plausible ordering to images that have been generated from a single image through transformations. Our approach learns transformation-aware descriptors using weak supervision via composited transformations and a rank-based quadruplet loss. To establish the efficacy of the proposed approach, comparisons with state-of-the-art handcrafted and deep learning-based descriptors, and image matching approaches are made. Further experimentation validates the proposed approach in the context of image provenance analysis. | ['Walter Scheirer', 'Aparna Bharati', 'Kevin Bowyer', 'Daniel Moreira', 'Anderson Rocha', 'Patrick Flynn'] | 2020-01-13 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.59942245e-01 -1.79203272e-01 -4.83432561e-02 -4.86302853e-01
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8889b213-dafd-4113-b94c-dcd3c31904c7 | news-driven-stock-prediction-using-noisy | null | null | https://openreview.net/forum?id=imnG4Ap9dAd | https://openreview.net/pdf?id=imnG4Ap9dAd | News-Driven Stock Prediction Using Noisy Equity State Representation | News-driven stock prediction investigates the correlation between news events and stock price movements.
Previous work has considered effective ways for representing news events and their sequences, but rarely exploited the representation of underlying equity states.
We address this issue by making use of a recurrent neural network to represent an equity state transition sequence, integrating news representation using contextualized embeddings as inputs to the state transition mechanism.
Thanks to the separation of news and equity representations, our model can accommodate additional input factors.
We design a novel random noise factor for modeling influencing factors beyond news events, and a future event factor to address the delay of news information (e.g., insider trading).
Results show that the proposed model outperforms strong baselines in the literature. | ['Yue Zhang', 'Heyan Huang', 'Xiao Liu'] | 2021-01-01 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.21813214e-01 6.12828927e-03 -7.44172573e-01 -3.86223882e-01
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1efa2041-6b82-49f0-bd77-c0085be5e76a | scene-labeling-with-lstm-recurrent-neural | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Byeon_Scene_Labeling_With_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Byeon_Scene_Labeling_With_2015_CVPR_paper.pdf | Scene Labeling With LSTM Recurrent Neural Networks | This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification. We investigate two-dimensional (2D) LSTM networks for natural scene images taking into account the complex spatial dependencies of labels. Prior methods generally have required separate classification and image segmentation stages and/or pre- and post-processing. In our approach, classification, segmentation, and context integration are all carried out by 2D LSTM networks, allowing texture and spatial model parameters to be learned within a single model. The networks efficiently capture local and global contextual information over raw RGB values and adapt well for complex scene images. Our approach, which has a much lower computational complexity than prior methods, achieved state-of-the-art performance over the Stanford Background and the SIFT Flow datasets. In fact, if no pre- or post-processing is applied, LSTM networks outperform other state-of-the-art approaches. Hence, only with a single-core Central Processing Unit (CPU), the running time of our approach is equivalent or better than the compared state-of-the-art approaches which use a Graphics Processing Unit (GPU). Finally, our networks' ability to visualize feature maps from each layer supports the hypothesis that LSTM networks are overall suited for image processing tasks. | ['Marcus Liwicki', 'Wonmin Byeon', 'Thomas M. Breuel', 'Federico Raue'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [ 5.24789989e-01 -4.09597307e-01 3.43449228e-02 -3.85760903e-01
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87431f5f-63e7-470c-8a4a-e4e2b3d95a8c | semantic-adversarial-network-for-zero-shot | 1905.02327 | null | https://arxiv.org/abs/1905.02327v2 | https://arxiv.org/pdf/1905.02327v2.pdf | Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario. Previous works mostly focus on modeling the correspondence between images and sketches or synthesizing image features with sketch features. However, both of them ignore the large intra-class variance of sketches, thus resulting in unsatisfactory retrieval performance. In this paper, we propose a novel end-to-end semantic adversarial approach for ZS-SBIR. Specifically, we devise a semantic adversarial module to maximize the consistency between learned semantic features and category-level word vectors. Moreover, to preserve the discriminability of synthesized features within each training category, a triplet loss is employed for the generative module. Additionally, the proposed model is trained in an end-to-end strategy to exploit better semantic features suitable for ZS-SBIR. Extensive experiments conducted on two large-scale popular datasets demonstrate that our proposed approach remarkably outperforms state-of-the-art approaches by more than 12\% on Sketchy dataset and about 3\% on TU-Berlin dataset in the retrieval. | ['Leida Li', 'Hao Wang', 'Xinxun Xu', 'Cheng Deng'] | 2019-05-07 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.20017046e-01 -4.28975075e-01 -1.68953180e-01 -2.45666683e-01
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e11cd528-8b24-4bb4-bd7b-007f8216c314 | potrika-raw-and-balanced-newspaper-datasets | 2210.09389 | null | https://arxiv.org/abs/2210.09389v1 | https://arxiv.org/pdf/2210.09389v1.pdf | Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes | Knowledge is central to human and scientific developments. Natural Language Processing (NLP) allows automated analysis and creation of knowledge. Data is a crucial NLP and machine learning ingredient. The scarcity of open datasets is a well-known problem in machine and deep learning research. This is very much the case for textual NLP datasets in English and other major world languages. For the Bangla language, the situation is even more challenging and the number of large datasets for NLP research is practically nil. We hereby present Potrika, a large single-label Bangla news article textual dataset curated for NLP research from six popular online news portals in Bangladesh (Jugantor, Jaijaidin, Ittefaq, Kaler Kontho, Inqilab, and Somoyer Alo) for the period 2014-2020. The articles are classified into eight distinct categories (National, Sports, International, Entertainment, Economy, Education, Politics, and Science \& Technology) providing five attributes (News Article, Category, Headline, Publication Date, and Newspaper Source). The raw dataset contains 185.51 million words and 12.57 million sentences contained in 664,880 news articles. Moreover, using NLP augmentation techniques, we create from the raw (unbalanced) dataset another (balanced) dataset comprising 320,000 news articles with 40,000 articles in each of the eight news categories. Potrika contains both the datasets (raw and balanced) to suit a wide range of NLP research. By far, to the best of our knowledge, Potrika is the largest and the most extensive dataset for news classification. | ['Rashid Mehmood', 'Fahad AlQurashi', 'Istiak Ahmad'] | 2022-10-17 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-5.67916155e-01 1.43894061e-01 -5.12676060e-01 -8.35831463e-02
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37d6b966-79f4-442a-9c9e-c5e3da70ebc3 | deeploc-a-ubiquitous-accurate-and-low | 2106.13632 | null | https://arxiv.org/abs/2106.13632v1 | https://arxiv.org/pdf/2106.13632v1.pdf | DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System | Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large areas, handling the inherent noise in the cellular signal and geo-tagged data, as well as providing enough data that is required for deep learning models with low-overhead. We implemented DeepLoc on different Android devices. Evaluation results in realistic urban and rural environments show that DeepLoc can achieve a median localization accuracy within 18.8m in urban areas and within 15.7m in rural areas. This accuracy outperforms the state-of-the-art cellular-based systems by more than 470% and comes with 330% savings in power compared to the GPS. This highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead localization system. | ['Moustafa Youssef', 'Marwan Torki', 'Ahmed Shokry'] | 2021-06-25 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-7.22621143e-01 -1.74129814e-01 -2.78115392e-01 -6.12981617e-02
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733abe1f-8d75-44ce-9074-e67733a523af | single-model-deep-learning-on-imbalanced | 2102.01284 | null | https://arxiv.org/abs/2102.01284v2 | https://arxiv.org/pdf/2102.01284v2.pdf | Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification | Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings. | ['Ronald X. Xu', 'Benjamin Kaffenberger', 'Pengfei Shao', 'Jinyu Xing', 'Fan Zhang', 'Peng Liu', 'Mengjuan Xu', 'Shuwei Shen', 'Peng Yao'] | 2021-02-02 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.49348831e-01 -6.29170462e-02 -4.05145705e-01 -3.34169388e-01
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bbe266c0-225c-4630-8318-50000b3dffc7 | joint-super-resolution-and-rectification-for | 2011.05003 | null | https://arxiv.org/abs/2011.05003v2 | https://arxiv.org/pdf/2011.05003v2.pdf | Joint Super-Resolution and Rectification for Solar Cell Inspection | Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this pre-processing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution (HR) pixels on the rectified module image becomes uniform w. r. t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state of the art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3x better than bicubic upsampling and 2x better than the state of the art for automated inspection. | ['Vincent Christlein', 'Andreas Maier', 'Christoph J. Brabec', 'Ian Marius Peters', 'Florian Talkenberg', 'Frank Schebesch', 'Bernd Doll', 'Thomas Köhler', 'Mathis Hoffmann'] | 2020-11-10 | null | null | null | null | ['crack-segmentation', 'multi-frame-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 9.05497134e-01 -2.34747276e-01 3.65590572e-01 7.18922094e-02
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df9c76d4-a9e2-4628-b50c-ec4d72d3b88b | endonet-a-deep-architecture-for-recognition | 1602.03012 | null | http://arxiv.org/abs/1602.03012v2 | http://arxiv.org/pdf/1602.03012v2.pdf | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos | Surgical workflow recognition has numerous potential medical applications,
such as the automatic indexing of surgical video databases and the optimization
of real-time operating room scheduling, among others. As a result, phase
recognition has been studied in the context of several kinds of surgeries, such
as cataract, neurological, and laparoscopic surgeries. In the literature, two
types of features are typically used to perform this task: visual features and
tool usage signals. However, the visual features used are mostly handcrafted.
Furthermore, the tool usage signals are usually collected via a manual
annotation process or by using additional equipment. In this paper, we propose
a novel method for phase recognition that uses a convolutional neural network
(CNN) to automatically learn features from cholecystectomy videos and that
relies uniquely on visual information. In previous studies, it has been shown
that the tool signals can provide valuable information in performing the phase
recognition task. Thus, we present a novel CNN architecture, called EndoNet,
that is designed to carry out the phase recognition and tool presence detection
tasks in a multi-task manner. To the best of our knowledge, this is the first
work proposing to use a CNN for multiple recognition tasks on laparoscopic
videos. Extensive experimental comparisons to other methods show that EndoNet
yields state-of-the-art results for both tasks. | ['Michel de Mathelin', 'Didier Mutter', 'Andru P. Twinanda', 'Sherif Shehata', 'Nicolas Padoy', 'Jacques Marescaux'] | 2016-02-09 | null | null | null | null | ['offline-surgical-phase-recognition', 'online-surgical-phase-recognition', 'surgical-tool-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.85848880e-01 -2.30879068e-01 -3.84081334e-01 -1.72665358e-01
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087f7565-f38d-43c7-94e3-8b9a207a4217 | txallo-dynamic-transaction-allocation-in | 2212.11584 | null | https://arxiv.org/abs/2212.11584v1 | https://arxiv.org/pdf/2212.11584v1.pdf | TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems | The scalability problem has been one of the most significant barriers limiting the adoption of blockchains. Blockchain sharding is a promising approach to this problem. However, the sharding mechanism introduces a significant number of cross-shard transactions, which are expensive to process. This paper focuses on the transaction allocation problem to reduce the number of cross-shard transactions for better scalability. In particular, we systematically formulate the transaction allocation problem and convert it to the community detection problem on a graph. A deterministic and fast allocation scheme TxAllo is proposed to dynamically infer the allocation of accounts and their associated transactions. It directly optimizes the system throughput, considering both the number of cross-shard transactions and the workload balance among shards. We evaluate the performance of TxAllo on an Ethereum dataset containing over 91 million transactions. Our evaluation results show that for a blockchain with 60 shards, TxAllo reduces the cross-shard transaction ratio from 98% (by using traditional hash-based allocation) to about 12%. In the meantime, the workload balance is well maintained. Compared with other methods, the execution time of TxAllo is almost negligible. For example, when updating the allocation every hour, the execution of TxAllo only takes 0.5 seconds on average, whereas other concurrent works, such as BrokerChain (INFOCOM'22) leveraging the classic METIS method, require 422 seconds. | ['Jiangshan Yu', 'Shirui Pan', 'Yuanzhe Zhang'] | 2022-12-22 | null | null | null | null | ['community-detection'] | ['graphs'] | [-3.99814963e-01 7.63842538e-02 -6.02476478e-01 3.90760526e-02
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c836f9e5-7fb5-4565-bbc5-5a80b28d418a | decision-attentive-regularization-to-improve | 2110.15729 | null | https://arxiv.org/abs/2110.15729v2 | https://arxiv.org/pdf/2110.15729v2.pdf | Decision Attentive Regularization to Improve Simultaneous Speech Translation Systems | Simultaneous translation systems start producing the output while processing the partial source sentence in the incoming input stream. These systems need to decide when to read more input and when to write the output. These decisions depend on the structure of source/target language and the information contained in the partial input sequence. Hence, read/write decision policy remains the same across different input modalities, i.e., speech and text. This motivates us to leverage the text transcripts corresponding to the speech input for improving simultaneous speech-to-text translation (SimulST). We propose Decision Attentive Regularization (DAR) to improve the decision policy of SimulST systems by using the simultaneous text-to-text translation (SimulMT) task. We also extend several techniques from the offline speech translation domain to explore the role of SimulMT task in improving SimulST performance. Overall, we achieve 34.66% / 4.5 BLEU improvement over the baseline model across different latency regimes for the MuST-C English-German (EnDe) SimulST task. | ['Sangha Kim', 'Chanwoo Kim', 'Beomseok Lee', 'Mohd Abbas Zaidi'] | 2021-10-13 | null | null | null | null | ['speech-to-text-translation', 'simultaneous-speech-to-text-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.08166957e-01 -1.41698569e-01 -3.33135307e-01 -3.53325307e-01
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bd044dd9-92a8-4779-80b4-f9afbe37fa77 | scene-graph-generation-from-hierarchical | 2303.06842 | null | https://arxiv.org/abs/2303.06842v1 | https://arxiv.org/pdf/2303.06842v1.pdf | Scene Graph Generation from Hierarchical Relationship Reasoning | This paper describes a novel approach to deducing relationships between objects in a visual scene. It explicitly exploits an informative hierarchical structure that can be imposed to divide the object and relationship categories into disjoint super-categories. Specifically, our proposed scheme implements a Bayes prediction head to jointly predict the super-category or type of relationship between the two objects, along with the detailed relationship within that super-category. This design reduces the impact of class imbalance problems. We present experimental results on the Visual Genome and OpenImage V6 datasets showing that this factorized approach allows a relatively simple model to achieve competitive performance, especially on predicate classification and zero-shot tasks. | ['Camillo J. Taylor', 'Bowen Jiang'] | 2023-03-13 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.88171798e-01 3.40811670e-01 -4.07562435e-01 -7.74019778e-01
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10a161a8-0a37-4fb7-94e5-1b6550735188 | learning-driven-exploration-for-reinforcement | 1906.06890 | null | https://arxiv.org/abs/1906.06890v2 | https://arxiv.org/pdf/1906.06890v2.pdf | Learning-Driven Exploration for Reinforcement Learning | Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $\epsilon$-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish the well explored and the unexplored regions of state space, which can lead to inefficient use of training time. We introduce entropy-based exploration (EBE) that enables an agent to explore efficiently the unexplored regions of state space. EBE quantifies the agent's learning in a state using merely state-dependent action values and adaptively explores the state space, i.e. more exploration for the unexplored region of the state space. We perform experiments on a diverse set of environments and demonstrate that EBE enables efficient exploration that ultimately results in faster learning without having to tune any hyperparameter. The code to reproduce the experiments is given at \url{https://github.com/Usama1002/EBE-Exploration} and the supplementary video is given at \url{https://youtu.be/nJggIjjzKic}. | ['Muhammad Usama', 'Dong Eui Chang'] | 2019-06-17 | null | null | null | null | ['fps-games'] | ['playing-games'] | [-1.67718396e-01 -6.38004020e-02 -5.03684878e-01 1.36680543e-01
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e093b4d1-d7f2-443e-8c82-b1a43d786dd9 | robust-m-estimation-based-bayesian-cluster | 2005.01404 | null | https://arxiv.org/abs/2005.01404v3 | https://arxiv.org/pdf/2005.01404v3.pdf | Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions | Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster enumeration criteria have been derived by formulating cluster enumeration as maximization of the posterior probability of candidate models. This article generalizes robust Bayesian cluster enumeration so that it can be used with any arbitrary Real Elliptically Symmetric (RES) distributed mixture model. Our framework also covers the case of M-estimators that allow for mixture models, which are decoupled from a specific probability distribution. Examples of Huber's and Tukey's M-estimators are discussed. We derive a robust criterion for data sets with finite sample size, and also provide an asymptotic approximation to reduce the computational cost at large sample sizes. The algorithms are applied to simulated and real-world data sets, including radar-based person identification, and show a significant robustness improvement in comparison to existing methods. | ['Christian A. Schroth', 'Michael Muma'] | 2020-05-04 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-1.79435648e-02 -3.28509480e-01 1.32158011e-01 -5.94347060e-01
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47873548-9c2e-440c-8b97-6fa9233b3d0b | multi-label-detection-and-classification-of | 1910.02672 | null | https://arxiv.org/abs/1910.02672v2 | https://arxiv.org/pdf/1910.02672v2.pdf | Multi-label Detection and Classification of Red Blood Cells in Microscopic Images | Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containing congregating cells) is more challenging, as separation of individual cells can be difficult (e.g. touching cells) or even impossible (e.g. overlapping cells). As multi-instance images are common in analyzing Red Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and implement a multi-instance cell detection and classification framework to address this challenge. The framework firstly trains a region proposal model based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of regions potentially containing single or multiple cells from input microscopic images, which are extracted as image patches. High-level image features are then calculated from image patches through a pre-trained Convolutional Neural Network (CNN) with ResNet-50 structure. Using these image features inputs, six networks are then trained to make multi-label prediction of whether a given patch contains cells belonging to a specific cell type. As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples. Finally, for the purpose of SCD testing, we train another machine learning classifier to predict whether the given image patch contains abnormal cell type based on outputs from the six networks. Testing result of the proposed framework shows that it can achieve good performance in automatic cell detection and classification. | ['Quanzheng Li', 'Wei Qiu', 'Mo Zhang', 'Mengjia Xu', 'Jiaming Guo', 'Xiang Li', 'Ning Guo'] | 2019-10-07 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.74938661e-01 -2.20407575e-01 1.38144732e-01 -1.39003426e-01
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91919cdb-9bb8-4fb7-81ab-454cfa831c82 | challenges-in-building-intelligent-open | 1905.05709 | null | https://arxiv.org/abs/1905.05709v3 | https://arxiv.org/pdf/1905.05709v3.pdf | Challenges in Building Intelligent Open-domain Dialog Systems | There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems. | ['Minlie Huang', 'Xiaoyan Zhu', 'Jianfeng Gao'] | 2019-05-13 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.09602767e-01 6.61552608e-01 6.75693303e-02 -7.95947313e-01
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0b9c4973-25e8-4e76-aa83-2d686b73e42f | a-supervised-geometry-aware-mapping-approach | 1807.02682 | null | http://arxiv.org/abs/1807.02682v1 | http://arxiv.org/pdf/1807.02682v1.pdf | A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images | The lack of proper class discrimination among the Hyperspectral (HS) data
points poses a potential challenge in HS classification. To address this issue,
this paper proposes an optimal geometry-aware transformation for enhancing the
classification accuracy. The underlying idea of this method is to obtain a
linear projection matrix by solving a nonlinear objective function based on the
intrinsic geometrical structure of the data. The objective function is
constructed to quantify the discrimination between the points from dissimilar
classes on the projected data space. Then the obtained projection matrix is
used to linearly map the data to more discriminative space. The effectiveness
of the proposed transformation is illustrated with three benchmark real-world
HS data sets. The experiments reveal that the classification and dimensionality
reduction methods on the projected discriminative space outperform their
counterpart in the original space. | ['S. L. Happy', 'Ramanarayan Mohanty', 'Aurobinda Routray'] | 2018-07-07 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 5.42233646e-01 -3.29128802e-01 2.53798533e-02 -3.64423811e-01
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0593fec2-252f-419d-826a-4ac0523aac5c | privacy-preserving-by-design-indoor | 2306.02211 | null | https://arxiv.org/abs/2306.02211v1 | https://arxiv.org/pdf/2306.02211v1.pdf | Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA | Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in a real-world testbed demonstrates PassiFi's exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy. | ['Moustafa Youssef', 'Hamada Rizk', 'Mohamed Mohsen'] | 2023-06-03 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-7.83093721e-02 -3.41747552e-01 1.60377882e-02 -5.05440056e-01
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ff1e06e0-c102-4350-9293-5edf7a3a81f1 | sparse-recovery-beyond-compressed-sensing | 1905.04627 | null | https://arxiv.org/abs/1905.04627v2 | https://arxiv.org/pdf/1905.04627v2.pdf | Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems | Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as a linear combination of functions that depend nonlinearly on certain parameters of interest. These parameters may represent neuronal activity in a human brain, frequencies of electromagnetic waves, fluorescent probes in a cell, or magnetic relaxation times of biological tissues. Separable nonlinear inverse problems can be reformulated as underdetermined sparse-recovery problems, and solved using convex programming. This approach has had empirical success in a variety of domains, from geophysics to medical imaging, but lacks a theoretical justification. In particular, compressed-sensing theory does not apply, because the measurement operators are deterministic and violate incoherence conditions such as the restricted-isometry property. Our main contribution is a theory for sparse recovery adapted to deterministic settings. We show that convex programming succeeds in recovering the parameters of interest, as long as their values are sufficiently distinct with respect to the correlation structure of the measurement operator. The theoretical results are illustrated through numerical experiments for two applications: heat-source localization and estimation of brain activity from electroencephalography data. | ['Carlos Fernandez-Granda', 'Chrysa Papadaniil', 'Sheng Liu', 'Brett Bernstein'] | 2019-05-12 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 6.16999328e-01 -2.11783692e-01 2.99055099e-01 -2.52151281e-01
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da38ca50-1506-4a28-bfc2-d2d0d8dab2ab | nonmyopic-view-planning-for-active-object | 1309.5401 | null | http://arxiv.org/abs/1309.5401v1 | http://arxiv.org/pdf/1309.5401v1.pdf | Nonmyopic View Planning for Active Object Detection | One of the central problems in computer vision is the detection of
semantically important objects and the estimation of their pose. Most of the
work in object detection has been based on single image processing and its
performance is limited by occlusions and ambiguity in appearance and geometry.
This paper proposes an active approach to object detection by controlling the
point of view of a mobile depth camera. When an initial static detection phase
identifies an object of interest, several hypotheses are made about its class
and orientation. The sensor then plans a sequence of views, which balances the
amount of energy used to move with the chance of identifying the correct
hypothesis. We formulate an active hypothesis testing problem, which includes
sensor mobility, and solve it using a point-based approximate POMDP algorithm.
The validity of our approach is verified through simulation and real-world
experiments with the PR2 robot. The results suggest that our approach
outperforms the widely-used greedy view point selection and provides a
significant improvement over static object detection. | ['Jerome Le Ny', 'Bharath Sankaran', 'Nikolay Atanasov', 'Kostas Daniilidis', 'George J. Pappas'] | 2013-09-20 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 4.67182308e-01 4.09133255e-01 -2.84179062e-01 -2.24632367e-01
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05c32a35-06ea-4bad-b0b8-5113302ad051 | from-renyi-entropy-power-to-information-scan | 2102.09415 | null | https://arxiv.org/abs/2102.09415v1 | https://arxiv.org/pdf/2102.09415v1.pdf | From Rényi Entropy Power to Information Scan of Quantum States | In the estimation theory context, we generalize the notion of Shannon's entropy power to the R\'{e}nyi-entropy setting. This not only allows to find new estimation inequalities, such as the R\'{e}nyi-entropy based De Bruijn identity, isoperimetric inequality or Stam inequality, but it also provides a convenient technical framework for the derivation of a one-parameter family of R\'{e}nyi-entropy-power-based quantum-mechanical uncertainty relations. To put more flesh on the bones, we use the R\'{e}nyi entropy power obtained to show how the information probability distribution associated with a quantum state can be reconstructed in a process that is akin to quantum-state tomography. We illustrate the inner workings of this with the so-called "cat states", which are of fundamental interest and practical use in schemes such as quantum metrology. Salient issues, including the extension of the notion of entropy power to Tsallis entropy and ensuing implications in estimation theory are also briefly discussed. | ['Martin Prokš', 'Jacob Dunningham', 'Petr Jizba'] | 2021-02-18 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.35399264e-01 4.41922277e-01 1.94331333e-02 -3.17324817e-01
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08b1583d-9e0c-433f-a142-5858bf94f066 | towards-neural-variational-monte-carlo-that | 2212.11296 | null | https://arxiv.org/abs/2212.11296v1 | https://arxiv.org/pdf/2212.11296v1.pdf | Towards Neural Variational Monte Carlo That Scales Linearly with System Size | Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, coupled with the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. However, the run-time of this approach scales quadratically with the number of simulated particles, constraining the practically usable NN to - in machine learning terms - minuscule sizes (<10M parameters). Considering the many breakthroughs brought by extreme NN in the +1B parameters scale to other domains, lifting this constraint could significantly expand the set of quantum systems we can accurately simulate on classical computers, both in size and complexity. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm - the source of the quadratic scaling. In our preliminary experiments, we demonstrate VQ-NQS ability to reproduce the ground state of the 2D Heisenberg model across various system sizes, while reporting a significant reduction of about ${\times}10$ in the number of FLOPs in the local-energy calculation. | ['Anima Anandkumar', 'Garnet Kin-Lic Chan', 'Or Sharir'] | 2022-12-21 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.08236873e-01 -1.22214623e-01 -3.93125042e-02 -8.53305832e-02
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ae730eeb-0f73-46dc-b536-8ace8a63b16d | towards-meta-learning-for-multi-target | 1907.11277 | null | https://arxiv.org/abs/1907.11277v1 | https://arxiv.org/pdf/1907.11277v1.pdf | Towards meta-learning for multi-target regression problems | Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the most suitable multi-target regression method. In this paper, we propose a meta-learning system to recommend the best predictive method for a given multi-target regression problem. We performed experiments with a meta-dataset generated by a total of 648 synthetic datasets. These datasets were created to explore distinct inter-targets characteristics toward recommending the most promising method. In experiments, we evaluated four different algorithms with different biases as meta-learners. Our meta-dataset is composed of 58 meta-features, based on: statistical information, correlation characteristics, linear landmarking, from the distribution and smoothness of the data, and has four different meta-labels. Results showed that induced meta-models were able to recommend the best methodfor different base level datasets with a balanced accuracy superior to 70% using a Random Forest meta-model, which statistically outperformed the meta-learning baselines. | ['Saulo Martiello Mastelini', 'Everton José Santana', 'Gabriel Jonas Aguiar', 'Sylvio Barbon Jr', 'Rafael Gomes Mantovani'] | 2019-07-25 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 1.62237003e-01 -1.76848099e-01 -8.28322649e-01 -6.06320322e-01
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53356746-c2c1-46b7-b880-c2e43f8c2c65 | attention-to-fires-multi-channel-deep | null | null | https://www.mdpi.com/2076-3417/11/22/11060 | https://www.mdpi.com/2076-3417/11/22/11060/pdf?version=1637581659 | Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction | Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attentionbased analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem. | ['Elena Baralis', 'Tania Cerquitelli', 'Paolo Garza', 'Daniele Apiletti', 'Luca Colomba', 'Alessandro Farasin', 'Salvatore Greco', 'Simone Monaco'] | 2021-11-22 | null | null | null | applied-sciences-2021-11 | ['severity-prediction'] | ['computer-vision'] | [ 4.07722920e-01 -3.90084349e-02 1.45444959e-01 -1.20610937e-01
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08077a3f-ad42-40b6-9ca5-1f68a85ed042 | router-for-wireless-power-packet-transmission | 2301.05368 | null | https://arxiv.org/abs/2301.05368v1 | https://arxiv.org/pdf/2301.05368v1.pdf | Router for wireless power packet transmission: Design and application to intersystem power management | Power supply for small-scale battery-powered systems such as electric vehicles (EVs and mobile robots) is being actively researched. We are particularly interested in energy management, which considers the interconnection of such systems close to each other. This allows for overall redundancy to be maintained without assuming excessive redundancy with individual power sources. Its implementation necessitates a high level of integration between power management and information and communication technology. As one of these methods, this study investigates energy management based on power packetization. When the individual systems to be connected have moving parts or are mobile, wireless power transmission is a promising method for power sharing. However, power packetization has so far only been considered for wired transmission. In this paper, we address the integration of power and information in wireless channels using power packetization. We propose a power packet router circuit that can wirelessly transmit power over multiple channels selectively. Furthermore, we demonstrate that the developed system can handle both wired intrasystem power management and wireless intersystem power sharing in a unified manner. | ['Takashi Hikihara', 'Shiu Mochiyama', 'Takahiro Mamiya'] | 2023-01-13 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.62848651e-01 2.37074286e-01 -8.55590165e-01 7.93493316e-02
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1026272b-3bfd-4b4a-9f0b-8f5a3144c1c3 | three-dimensional-ultrasound-matrix-imaging | 2303.07483 | null | https://arxiv.org/abs/2303.07483v1 | https://arxiv.org/pdf/2303.07483v1.pdf | Three-Dimensional Ultrasound Matrix Imaging | Matrix imaging paves the way towards a next revolution in wave physics. Based on the response matrix recorded between a set of sensors, it enables an optimized compensation of aberration phenomena and multiple scattering events that usually drastically hinder the focusing process in heterogeneous media. Although it gave rise to spectacular results in optical microscopy or seismic imaging, the success of matrix imaging has been so far relatively limited with ultrasonic waves because wave control is generally only performed with a linear array of transducers. In this paper, we extend ultrasound matrix imaging to a 3D geometry. Switching from a 1D to a 2D probe enables a much sharper estimation of the transmission matrix that links each transducer and each medium voxel. Here, we first present an experimental proof of concept on a tissue-mimicking phantom through ex-vivo tissues and then, show the potential of 3D matrix imaging for transcranial applications. | ['Alexandre Aubry', 'Mathias Fink', 'William Lambert', 'Arthur Le Ber', 'Justine Robin', 'Flavien Bureau'] | 2023-03-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 6.03657424e-01 4.02293429e-02 7.58631825e-01 1.63430974e-01
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30b37b2a-9b54-495d-98ef-ec3c05266c03 | ernie-sparse-learning-hierarchical-efficient | null | null | https://openreview.net/forum?id=Nctx6hVAQYf | https://openreview.net/pdf?id=Nctx6hVAQYf | ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention | Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer. This paper proposes a well-designed model named ERNIE-Sparse. It consists of two distinctive parts: (i) Hierarchical Sparse Transformer (HST) to sequentially unify local and global information. (ii) Self-Attention Regularization (SAR) method, a novel regularization designed to minimize the distance for transformers with different attention topologies. To evaluate the effectiveness of ERNIE-Sparse, we perform extensive evaluations. Firstly, we perform experiments on a multi-modal long sequence modeling task benchmark, Long Range Arena (LRA). Experimental results demonstrate that ERNIE-Sparse significantly outperforms a variety of strong baseline methods including the dense attention and other efficient sparse attention methods and achieves improvements by 2.77% (57.78% vs. 55.01%). Secondly, to further show the effectiveness of our method, we pretrain ERNIE-Sparse and verified it on 3 text classification and 2 QA downstream tasks, achieve improvements on classification benchmark by 0.83% (92.46% vs. 91.63%), on QA benchmark by 3.24% (74.67% vs. 71.43%). Experimental results continue to demonstrate its superior performance.
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8b54b9fc-d7f3-458f-9dcc-168ed4db172b | distributed-graph-embedding-with-information | 2303.15702 | null | https://arxiv.org/abs/2303.15702v1 | https://arxiv.org/pdf/2303.15702v1.pdf | Distributed Graph Embedding with Information-Oriented Random Walks | Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33x-129x acceleration, 45% reduction in cross-machines communication, and > 10% effectiveness improvement in downstream tasks. | ['Yuchao Cao', 'Wei Yin', 'Zhenli Li', 'Dan Feng', 'Fang Wang', 'Siqiang Luo', 'Arijit Khan', 'Peng Fang'] | 2023-03-28 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-7.73740709e-01 6.38886467e-02 -4.91275549e-01 6.20160513e-02
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8fba5534-f661-4272-9ea3-7727ce2a5610 | iit-gandhinagar-at-semeval-2020-task-9-code | 2006.14465 | null | https://arxiv.org/abs/2006.14465v3 | https://arxiv.org/pdf/2006.14465v3.pdf | IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection | Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech. It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier. The results present an opportunity to understand various other nuances of code-mixing in the textual data, such as humor-detection, intent classification, etc. | ['Vivek Srivastava', 'Mayank Singh'] | 2020-06-25 | null | https://aclanthology.org/2020.semeval-1.168 | https://aclanthology.org/2020.semeval-1.168.pdf | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [ 6.50665686e-02 -1.37630418e-01 -6.69819713e-02 -3.95488322e-01
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458034e5-cd14-421e-91fa-c6467244294b | forensic-discrimination-between-traditional | 1811.03157 | null | http://arxiv.org/abs/1811.03157v1 | http://arxiv.org/pdf/1811.03157v1.pdf | Forensic Discrimination between Traditional and Compressive Imaging Systems | Compressive sensing is a new technology for modern computational imaging
systems. In comparison to widespread conventional image sensing, the
compressive imaging paradigm requires specific forensic analysis techniques and
tools. In this regards, one of basic scenarios in image forensics is to
distinguish traditionally sensed images from sophisticated compressively sensed
ones. To do this, we first mathematically and systematically model the imaging
system based on compressive sensing technology. Afterwards, a simplified
version of the whole model is presented, which is appropriate for forensic
investigation applications. We estimate the nonlinear system of compressive
sensing with a linear model. Then, we model the imaging pipeline as an inverse
problem and demonstrate that different imagers have discriminative degradation
kernels. Hence, blur kernels of various imaging systems have utilized as
footprints for discriminating image acquisition sources. In order to accomplish
the identification cycle, we have utilized the state-of-the-art Convolutional
Neural Network (CNN) and Support Vector Machine (SVM) approaches to learn a
classification system from estimated blur kernels. Numerical experiments show
promising identification results. Simulation codes are available for research
and development purposes. | ['Farokh Marvasti', 'Ali Taimori'] | 2018-11-07 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.81473279e-01 -5.80552340e-01 1.27538502e-01 2.99806852e-04
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63da9045-83a8-42b4-af68-f76c30ad2709 | properties-on-n-dimensional-convolution-for | 1711.11224 | null | http://arxiv.org/abs/1711.11224v1 | http://arxiv.org/pdf/1711.11224v1.pdf | Properties on n-dimensional convolution for image deconvolution | Convolution system is linear and time invariant, and can describe the optical
imaging process. Based on convolution system, many deconvolution techniques
have been developed for optical image analysis, such as boosting the space
resolution of optical images, image denoising, image enhancement and so on.
Here, we gave properties on N-dimensional convolution. By using these
properties, we proposed image deconvolution method. This method uses a series
of convolution operations to deconvolute image. We demonstrated that the method
has the similar deconvolution results to the state-of-art method. The core
calculation of the proposed method is image convolution, and thus our method
can easily be integrated into GPU mode for large-scale image deconvolution. | ['Zhou Hang', 'Song Yizhi', 'Ding Daoxin', 'Xu Cheng', 'Li Shiwei', 'Quan Tingwei'] | 2017-11-30 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 8.57826173e-02 -7.83512831e-01 7.92651713e-01 -1.35940447e-01
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5c813664-327e-4357-8657-d60e4ea7527d | image-animation-with-refined-masking | null | null | https://openreview.net/forum?id=VKoY98_IN3- | https://openreview.net/pdf?id=VKoY98_IN3- | Image Animation with Refined Masking | We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without knowledge of the object's structure. Furthermore, both the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object’s general pose and shape. A mask-refinement module then replaces, in the mask extracted from the driver image, the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Our method is shown to greatly outperform the state of the art methods on multiple benchmarks. Our code and samples are attached as supplementary. | ['Lior Wolf', 'Yoav Shalev'] | 2021-01-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 6.41924918e-01 3.33254397e-01 5.09875119e-01 5.05563319e-02
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0923de4d-3e47-4610-92ef-f3267e2226c2 | towards-data-driven-sign-language | null | null | https://openreview.net/forum?id=HpQA6JhTL7x | https://openreview.net/pdf?id=HpQA6JhTL7x | Towards data-driven sign language interpreting virtual assistant | Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have seen the increase in the use of virtual agents as assistants for the sign language users. Research into sign language recognition has demonstrated promising potential for the improvement of the communication with the deaf people. However, the area of sign language synthesis is still in its infancy. This explains the underdevelopment of the virtual intelligent signing systems, which could bridge the communication with the deaf and make it more favorable. In addition, the existing models are often restricted to the manually written rules and require expert knowledge, while data-driven approach could provide a better solution.
In this paper, we present a user study on the evaluation of the data-driven Virtual Assistant that performs signing gestures for the Kazakh-Russian sign language using sign sequences. The study sets out to answer three research questions concerning the users' perceptions and feedback on the performance of the four signing agents, namely two data-driven avatars, one motion capture animation avatar and a human sign interpreter. The results of the questionnaire suggest that while signing avatars generally perform well, they could not outperform the human agent in terms of naturalness and likeability. Hence, a further study might include the improvements necessary to increase the naturalness of the signing gestures. | ['Anonymous'] | 2021-06-23 | null | null | null | acm-icmi-workshop-genea-2021-10 | ['sign-language-recognition'] | ['computer-vision'] | [-2.09153920e-01 2.79762864e-01 -7.85148963e-02 -3.89285654e-01
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-1.56992093e-01 1.05433136e-01 2.76684433e-01 -6.47873461e-01
2.87288696e-01 -4.11983609e-01 -4.06634063e-01 -3.97912890e-01
2.76606381e-01 -1.17011130e+00 1.63730100e-01 1.01214528e+00
6.42029643e-02 -1.79760203e-01 -1.54721677e-01 -1.16209015e-01
-2.25965023e-01 -3.13765258e-01 5.67589283e-01 1.85167473e-02
-1.08754170e+00 -3.35427374e-01 -9.41529632e-01 -4.39927280e-01
1.11177933e+00 -1.07750499e+00 1.86323151e-02 -8.62244666e-01
-5.11120677e-01 -6.93864599e-02 4.12128597e-01 4.23552960e-01
5.81765652e-01 -9.61270988e-01 -5.23511350e-01 3.53435665e-01
2.18877122e-01 -4.60080057e-01 -2.19018757e-01 6.34634197e-01
-1.24601674e+00 4.47438031e-01 -7.16054082e-01 -4.67116609e-02
-1.99564064e+00 -1.34604096e-01 2.36013129e-01 3.29532593e-01
-7.63797045e-01 5.53898454e-01 -3.64390314e-01 -3.79197091e-01
8.23046088e-01 -4.66928124e-01 -6.09071970e-01 9.72819030e-02
4.58853602e-01 7.00229526e-01 -4.95143473e-01 -1.24075842e+00
-4.36092287e-01 7.41217554e-01 2.92070299e-01 -8.28653872e-01
1.12246919e+00 1.30500168e-01 2.12708592e-01 3.02493900e-01
6.02530718e-01 1.28380120e-01 -7.04084933e-01 1.50197506e-01
2.62657642e-01 -6.66430891e-01 -2.53060848e-01 -1.05687332e+00
-6.47937179e-01 6.01078749e-01 1.07920885e+00 -5.55063896e-02
8.46787930e-01 -1.15090899e-01 3.85953605e-01 5.39805233e-01
7.22929478e-01 -1.11231768e+00 -3.62739503e-01 4.30449992e-01
1.23929012e+00 -1.18445265e+00 -3.44325513e-01 -2.35957190e-01
-8.68589044e-01 1.24666655e+00 5.20707667e-01 2.55903721e-01
5.62649786e-01 3.78778368e-01 9.55162764e-01 -3.03784579e-01
1.54691981e-02 -5.73599756e-01 5.41396774e-02 1.21616769e+00
7.80164778e-01 5.09038389e-01 -1.40229535e+00 3.77157032e-01
-4.12427515e-01 5.39760113e-01 6.31920040e-01 1.22142279e+00
-6.49309278e-01 -1.34583080e+00 -7.46709049e-01 5.16353846e-02
2.16490790e-01 5.30852497e-01 -7.55459011e-01 1.36627960e+00
1.72604561e-01 1.13819730e+00 -4.97542620e-01 -2.76874900e-01
1.01326287e+00 2.56476909e-01 5.48953176e-01 -1.60736859e-01
-4.65511948e-01 -2.00048491e-01 4.10003006e-01 -1.10615656e-01
-5.74172854e-01 -9.85166490e-01 -1.58239746e+00 -2.35064879e-01
-1.54571787e-01 -4.12548408e-02 1.21064162e+00 1.05586636e+00
-4.33749259e-01 -8.13182909e-03 6.59493357e-02 -7.84933031e-01
-5.43274283e-01 -1.23930132e+00 -5.44061124e-01 4.52269018e-01
2.94068247e-01 -4.98529822e-01 -3.01961184e-01 -8.00337866e-02] | [9.078335762023926, -6.375583648681641] |
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