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daa2fbe1-ba62-4131-8e31-473ea9bf9d8f | memguard-defending-against-black-box | 1909.10594 | null | https://arxiv.org/abs/1909.10594v3 | https://arxiv.org/pdf/1909.10594v3.pdf | MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples | In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target classifier as an input and predicts the data sample to be a member or non-member of the target classifier's training dataset. Membership inference attacks pose severe privacy and security threats to the training dataset. Most existing defenses leverage differential privacy when training the target classifier or regularize the training process of the target classifier. These defenses suffer from two key limitations: 1) they do not have formal utility-loss guarantees of the confidence score vectors, and 2) they achieve suboptimal privacy-utility tradeoffs. In this work, we propose MemGuard, the first defense with formal utility-loss guarantees against black-box membership inference attacks. Instead of tampering the training process of the target classifier, MemGuard adds noise to each confidence score vector predicted by the target classifier. Our key observation is that attacker uses a classifier to predict member or non-member and classifier is vulnerable to adversarial examples. Based on the observation, we propose to add a carefully crafted noise vector to a confidence score vector to turn it into an adversarial example that misleads the attacker's classifier. Our experimental results on three datasets show that MemGuard can effectively defend against membership inference attacks and achieve better privacy-utility tradeoffs than existing defenses. Our work is the first one to show that adversarial examples can be used as defensive mechanisms to defend against membership inference attacks. | ['Neil Zhenqiang Gong', 'Ahmed Salem', 'Jinyuan Jia', 'Yang Zhang', 'Michael Backes'] | 2019-09-23 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.34222519e-01 2.55597979e-01 -4.31125075e-01 -4.13173854e-01
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b4e0673b-07c2-4610-b5a1-1cf0f3f434a5 | yelan-event-camera-based-3d-human-pose | 2301.06648 | null | https://arxiv.org/abs/2301.06648v2 | https://arxiv.org/pdf/2301.06648v2.pdf | Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment | As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth cameras for dance games. The former suffers in low-lighting conditions due to the motion blur and low sensitivity, while the latter is too power-hungry, has a low frame rate, and has limited working distance. With ultra-low latency, energy efficiency, and wide dynamic range characteristics, the event camera is a promising solution to overcome these shortcomings. We propose YeLan, an event camera-based 3-dimensional high-frequency human pose estimation(HPE) system that survives low-lighting conditions and dynamic backgrounds. We collected the world's first event camera dance dataset and developed a fully customizable motion-to-event physics-aware simulator. YeLan outperforms the baseline models in these challenging conditions and demonstrated robustness against different types of clothing, background motion, viewing angle, occlusion, and lighting fluctuations. | ['Tauhidur Rahman', 'Donghyun Kim', 'Hava Siegelmann', 'Upal Mahbub', 'Edward Wang', 'Francesca Walsh', 'Ramzi Majaj', 'Haowen Yu', 'Kaidong Chai', 'Zhongyang Zhang'] | 2023-01-17 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.83318490e-01 -6.67081892e-01 1.51723683e-01 3.91491205e-02
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639ae6d1-4d8f-4058-8b0e-d43d4d536e7a | facemap-towards-unsupervised-face-clustering | 2203.10090 | null | https://arxiv.org/abs/2203.10090v1 | https://arxiv.org/pdf/2203.10090v1.pdf | FaceMap: Towards Unsupervised Face Clustering via Map Equation | Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. The main challenge of this task lies in the imperfectness of similarities among image feature representations. Given an existing feature extraction model, it is still an unresolved problem that how can the inherent characteristics of similarities of unlabelled images be leveraged to improve the clustering performance. Motivated by answering the question, we develop an effective unsupervised method, named as FaceMap, by formulating face clustering as a process of non-overlapping community detection, and minimizing the entropy of information flows on a network of images. The entropy is denoted by the map equation and its minimum represents the least description of paths among images in expectation. Inspired by observations on the ranked transition probabilities in the affinity graph constructed from facial images, we develop an outlier detection strategy to adaptively adjust transition probabilities among images. Experiments with ablation studies demonstrate that FaceMap significantly outperforms existing methods and achieves new state-of-the-arts on three popular large-scale datasets for face clustering, e.g., an absolute improvement of more than $10\%$ and $4\%$ comparing with prior unsupervised and supervised methods respectively in terms of average of Pairwise F-score. Our code is publicly available on github. | ['Xiaoyu Wang', 'Guangming Lu', 'Hanling Yi', 'Ling Xing', 'Aibo Wang', 'Yifan Yang', 'Xiaotian Yu'] | 2022-03-21 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.98855087e-01 6.51236176e-02 1.21652864e-01 -5.07209957e-01
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0bfb27e3-0d27-4f31-8668-9654386ec6f9 | natural-language-guided-visual-relationship | 1711.06032 | null | http://arxiv.org/abs/1711.06032v2 | http://arxiv.org/pdf/1711.06032v2.pdf | Natural Language Guided Visual Relationship Detection | Reasoning about the relationships between object pairs in images is a crucial
task for holistic scene understanding. Most of the existing works treat this
task as a pure visual classification task: each type of relationship or phrase
is classified as a relation category based on the extracted visual features.
However, each kind of relationships has a wide variety of object combination
and each pair of objects has diverse interactions. Obtaining sufficient
training samples for all possible relationship categories is difficult and
expensive. In this work, we propose a natural language guided framework to
tackle this problem. We propose to use a generic bi-directional recurrent
neural network to predict the semantic connection between the participating
objects in the relationship from the aspect of natural language. The proposed
simple method achieves the state-of-the-art on the Visual Relationship
Detection (VRD) and Visual Genome datasets, especially when predicting unseen
relationships (e.g. recall improved from 76.42% to 89.79% on VRD zero-shot
testing set). | ['Bodo Rosenhahn', 'Wentong Liao', 'Lin Shuai', 'Michael Ying Yang'] | 2017-11-16 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.40003800e-01 -9.09669548e-02 -2.33005673e-01 -4.41447526e-01
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4cd0ac3e-a5ee-4947-965d-44340d621a6e | unsupervised-hdr-imaging-what-can-be-learned | 2202.05522 | null | https://arxiv.org/abs/2202.05522v1 | https://arxiv.org/pdf/2202.05522v1.pdf | Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video? | Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. Typically, these methods, to be effective, need to learn from large datasets and to transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR video? With the presented zero-shot approach, we show that, in many cases, a single SDR video is sufficient to be able to generate an HDR video of the same quality or better than other state-of-the-art methods. | ['Thomas Bashford-Rogers', 'Kurt Debattista', 'Demetris Marnerides', 'Francesco Banterle'] | 2022-02-11 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.90582132e-01 -6.49471655e-02 1.37432560e-01 -1.94546118e-01
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5bacaa20-43dd-4caf-8052-809a27270df0 | swahbert-language-model-of-swahili | null | null | https://aclanthology.org/2022.naacl-main.23 | https://aclanthology.org/2022.naacl-main.23.pdf | SwahBERT: Language Model of Swahili | The rapid development of social networks, electronic commerce, mobile Internet, and other technologies, has influenced the growth of Web data.Social media and Internet forums are valuable sources of citizens’ opinions, which can be analyzed for community development and user behavior analysis.Unfortunately, the scarcity of resources (i.e., datasets or language models) become a barrier to the development of natural language processing applications in low-resource languages.Thanks to the recent growth of online forums and news platforms of Swahili, we introduce two datasets of Swahili in this paper: a pre-training dataset of approximately 105MB with 16M words and annotated dataset of 13K instances for the emotion classification task.The emotion classification dataset is manually annotated by two native Swahili speakers.We pre-trained a new monolingual language model for Swahili, namely SwahBERT, using our collected pre-training data, and tested it with four downstream tasks including emotion classification.We found that SwahBERT outperforms multilingual BERT, a well-known existing language model, in almost all downstream tasks. | ['Jeong Young-Seob', 'Young-Seob Jeong', 'Medard Edmund Mswahili', 'Gati Martin'] | null | null | null | null | naacl-2022-7 | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-6.10873103e-01 -2.36250520e-01 -3.23386431e-01 -4.67270523e-01
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5e491994-cb8b-47db-a298-38d3171b3f05 | comparing-cross-correlation-based | 2111.08513 | null | https://arxiv.org/abs/2111.08513v2 | https://arxiv.org/pdf/2111.08513v2.pdf | Comparing Cross Correlation-Based Similarities | The real-valued Jaccard and coincidence indices, in addition to their conceptual and computational simplicity, have been verified to be able to provide promising results in tasks such as template matching, tending to yield peaks that are sharper and narrower than those typically obtained by standard cross-correlation, while also attenuating substantially secondary matchings. In this work, the multiset-based correlations based on the real-valued multiset Jaccard and coincidence indices are compared from the perspective of template matching, with encouraging results which have implications for pattern recognition, deep learning, and scientific modeling in general. The multiset-based correlation methods, and especially the coincidence index, presented remarkable performance characterized by sharper and narrower peaks while secondary peaks were attenuated, which was maintained even in presence of intense levels of noise. In particular, the two methods derived from the coincidence index led to particularly interesting results. The cross correlation, however, presented the best robustness to symmetric additive noise, which suggested a new combination of the considered approaches. After a preliminary investigation of the relative performance of the multiset approaches, as well as the classic cross-correlation, a systematic comparison framework is proposed and applied for the study of the aforementioned methods. Several results are reported, including the confirmation, at least for the considered type of data, of the coincidence correlation as providing enhanced performance regarding detection of narrow, sharp peaks while secondary matches are duly attenuated. The combined method also resulted promising for dealing with signals in presence of intense additive noise. | ['Luciano da F. Costa'] | 2021-11-08 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.15262049e-01 -1.71968356e-01 2.76976436e-01 -7.82300383e-02
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e09c079a-0e82-4a5f-9d46-bde37d19410e | inpaintnerf360-text-guided-3d-inpainting-on | 2305.15094 | null | https://arxiv.org/abs/2305.15094v1 | https://arxiv.org/pdf/2305.15094v1.pdf | InpaintNeRF360: Text-Guided 3D Inpainting on Unbounded Neural Radiance Fields | Neural Radiance Fields (NeRF) can generate highly realistic novel views. However, editing 3D scenes represented by NeRF across 360-degree views, particularly removing objects while preserving geometric and photometric consistency, remains a challenging problem due to NeRF's implicit scene representation. In this paper, we propose InpaintNeRF360, a unified framework that utilizes natural language instructions as guidance for inpainting NeRF-based 3D scenes.Our approach employs a promptable segmentation model by generating multi-modal prompts from the encoded text for multiview segmentation. We apply depth-space warping to enforce viewing consistency in the segmentations, and further refine the inpainted NeRF model using perceptual priors to ensure visual plausibility. InpaintNeRF360 is capable of simultaneously removing multiple objects or modifying object appearance based on text instructions while synthesizing 3D viewing-consistent and photo-realistic inpainting. Through extensive experiments on both unbounded and frontal-facing scenes trained through NeRF, we demonstrate the effectiveness of our approach and showcase its potential to enhance the editability of implicit radiance fields. | ['Sabine Süsstrunk', 'Alaa Abboud', 'Tong Zhang', 'Dongqing Wang'] | 2023-05-24 | null | null | null | null | ['3d-inpainting'] | ['computer-vision'] | [ 7.68643498e-01 3.01365823e-01 4.27483052e-01 -5.09029329e-01
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48773e43-cf0d-4c0e-818f-01e36aa72765 | shrinkml-end-to-end-asr-model-compression | 1907.03540 | null | https://arxiv.org/abs/1907.03540v2 | https://arxiv.org/pdf/1907.03540v2.pdf | ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning | End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses reinforcement learning (RL) to optimize the per-layer compression ratios when applied to a state-of-the-art attention based end-to-end ASR model composed of several LSTM layers. We use singular value decomposition (SVD) low-rank matrix factorization as the compression method. For our RL-based AutoML system, we focus on practical considerations such as the choice of the reward/punishment functions, the formation of an effective search space, and the creation of a representative but small data set for quick evaluation between search steps. Finally, we present accuracy results on LibriSpeech of the model compressed by our AutoML system, and we compare it to manually-compressed models. Our results show that in the absence of retraining our RL-based search is an effective and practical method to compress a production-grade ASR system. When retraining is possible, we show that our AutoML system can select better highly-compressed seed models compared to manually hand-crafted rank selection, thus allowing for more compression than previously possible. | ['Łukasz Dudziak', 'Nicholas D. Lane', 'Mohamed S. Abdelfattah', 'Stefanos Laskaridis', 'Ravichander Vipperla'] | 2019-07-08 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.25565857e-01 -6.13111211e-03 -6.49619475e-02 -1.99718788e-01
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3a8824eb-609f-45c4-bc81-50ccb93a2f6d | towards-a-flexible-deep-learning-method-for | 1905.08059 | null | https://arxiv.org/abs/1905.08059v1 | https://arxiv.org/pdf/1905.08059v1.pdf | Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram | Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model. | ['Stanislas Chambon', 'Poul Jennum', 'Emmanuel Mignot', 'Valentin Thorey', 'Helge B. D. Sorensen', 'Alexander Neergaard Olesen'] | 2019-05-16 | null | null | null | null | ['multimodal-sleep-stage-detection', 'sleep-staging'] | ['medical', 'medical'] | [ 1.06959455e-01 3.12712491e-02 -1.58468876e-02 -6.22621238e-01
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1cf43314-cf1a-49d4-b740-bb559a1b9b54 | best-of-three-worlds-analysis-for-linear | 2303.06825 | null | https://arxiv.org/abs/2303.06825v1 | https://arxiv.org/pdf/2303.06825v1.pdf | Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm | The linear bandit problem has been studied for many years in both stochastic and adversarial settings. Designing an algorithm that can optimize the environment without knowing the loss type attracts lots of interest. \citet{LeeLWZ021} propose an algorithm that actively detects the loss type and then switches between different algorithms specially designed for different settings. However, such an approach requires meticulous designs to perform well in all settings. Follow-the-regularized-leader (FTRL) is another popular algorithm type that can adapt to different environments. This algorithm is of simple design and the regret bounds are shown to be optimal in traditional multi-armed bandit problems compared with the detect-switch type algorithms. Designing an FTRL-type algorithm for linear bandits is an important question that has been open for a long time. In this paper, we prove that the FTRL-type algorithm with a negative entropy regularizer can achieve the best-of-three-world results for the linear bandit problem with the tacit cooperation between the choice of the learning rate and the specially designed self-bounding inequality. | ['Shuai Li', 'Canzhe Zhao', 'Fang Kong'] | 2023-03-13 | null | null | null | null | ['type'] | ['speech'] | [ 4.04565372e-02 1.02284133e-01 -7.88365245e-01 -3.35200578e-01
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0044a742-bfb6-4c77-9536-68da4e275e68 | robot-task-planning-based-on-large-language | 2306.05171 | null | https://arxiv.org/abs/2306.05171v1 | https://arxiv.org/pdf/2306.05171v1.pdf | Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures | Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured professional knowledge. We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task, and we have designed a strategy to decouple robot task planning. By dividing different planning entities and separating the task from the actual machine binding process, the task planning process becomes more flexible. Research results show that our method performs well in handling specified code formats, understanding the relationship between tasks and subtasks, and extracting parameters from text descriptions. However, there are also problems such as limited complexity of task logic handling, ambiguity in the quantity of parts and the precise location of assembly. Improving the precision of task description and cognitive structure can bring certain improvements. https://github.com/NOMIzy/Think_Net_Prompt | ['Fang Yi-shu', 'Chen Zi-rui', 'Shi Hai-peng', 'Pan Wei-qin', 'Lu Xing-tong', 'Sheng Bi', 'Yue Zhen'] | 2023-06-08 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 7.49200955e-02 4.81632531e-01 1.69603094e-01 -4.55861568e-01
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7b5e005b-4392-45af-b089-a4eea2ee09ee | automatic-gesture-recognition-in-robot | 2002.08718 | null | https://arxiv.org/abs/2002.08718v1 | https://arxiv.org/pdf/2002.08718v1.pdf | Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search | Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation. However, current methods treat each frame individually and produce the outcomes without effective consideration on future information. In this paper, we propose a framework based on reinforcement learning and tree search for joint surgical gesture segmentation and classification. An agent is trained to segment and classify the surgical video in a human-like manner whose direct decisions are re-considered by tree search appropriately. Our proposed tree search algorithm unites the outputs from two designed neural networks, i.e., policy and value network. With the integration of complementary information from distinct models, our framework is able to achieve the better performance than baseline methods using either of the neural networks. For an overall evaluation, our developed approach consistently outperforms the existing methods on the suturing task of JIGSAWS dataset in terms of accuracy, edit score and F1 score. Our study highlights the utilization of tree search to refine actions in reinforcement learning framework for surgical robotic applications. | ['Pheng-Ann Heng', 'Xiaojie Gao', 'Qi Dou', 'Yueming Jin'] | 2020-02-20 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 5.88014901e-01 4.24989998e-01 -4.48312074e-01 -4.07749563e-01
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2f5483e0-a35b-4795-ab13-e248346a557e | comparing-deep-learning-models-for-multi-cell | 1910.00722 | null | https://arxiv.org/abs/1910.00722v1 | https://arxiv.org/pdf/1910.00722v1.pdf | Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images | Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting deep hierarchical features by fine-tuning convolution neural networks, as well as a novel graph-based cell detection approach for cellular level evaluation. The results show that the proposed pipeline can classify images of both single cell and overlapping cells. The VGG-19 model is found to be the best at classifying the cervical cytology patch data with 95 % accuracy under precision-recall curve. | ['Sudhir Sornapudi', 'Lisa Allen', 'Zhiyun Xue', 'Sameer Antani', 'G. T. Brown', 'Rodney Long'] | 2019-10-02 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.37818116e-01 1.21872880e-01 -1.73323229e-01 -8.30597207e-02
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f44d4da9-2cf8-42ad-af7c-832e3554e64b | scenegraphnet-neural-message-passing-for-3d | 1907.11308 | null | https://arxiv.org/abs/1907.11308v1 | https://arxiv.org/pdf/1907.11308v1.pdf | SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation | In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings. Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that location. Our distribution is predicted though passing learned messages in a dense graph whose nodes represent objects in the input scene and edges represent spatial and structural relationships. By weighting messages through an attention mechanism, our method learns to focus on the most relevant surrounding scene context to predict new scene objects. We found that our method significantly outperforms state-of-the-art approaches in terms of correctly predicting objects missing in a scene based on our experiments in the SUNCG dataset. We also demonstrate other applications of our method, including context-based 3D object recognition and iterative scene generation. | ['Evangelos Kalogerakis', 'Yang Zhou', 'Zachary While'] | 2019-07-25 | scenegraphnet-neural-message-passing-for-3d-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_SceneGraphNet_Neural_Message_Passing_for_3D_Indoor_Scene_Augmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_SceneGraphNet_Neural_Message_Passing_for_3D_Indoor_Scene_Augmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-recognition', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 0.5689176 0.05201081 0.15315624 -0.8021307 -0.5889449 -0.35642007
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da7d69c5-df16-49cc-873c-581a33f2e0a3 | a-generalist-framework-for-panoptic | 2210.06366 | null | https://arxiv.org/abs/2210.06366v2 | https://arxiv.org/pdf/2210.06366v2.pdf | A Generalist Framework for Panoptic Segmentation of Images and Videos | Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image. As permutations of instance IDs are also valid solutions, the task requires learning of high-dimensional one-to-many mapping. As a result, state-of-the-art approaches use customized architectures and task-specific loss functions. We formulate panoptic segmentation as a discrete data generation problem, without relying on inductive bias of the task. A diffusion model based on analog bits is used to model panoptic masks, with a simple, generic architecture and loss function. By simply adding past predictions as a conditioning signal, our method is capable of modeling video (in a streaming setting) and thereby learns to track object instances automatically. With extensive experiments, we demonstrate that our generalist approach can perform competitively to state-of-the-art specialist methods in similar settings. | ['David J. Fleet', 'Geoffrey Hinton', 'Saurabh Saxena', 'Lala Li', 'Ting Chen'] | 2022-10-12 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 8.96170855e-01 2.23274902e-01 -2.77204126e-01 -4.52979416e-01
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303c734c-c146-42ec-85eb-84183c8cef30 | understanding-text-driven-motion-synthesis | 2305.13773 | null | https://arxiv.org/abs/2305.13773v1 | https://arxiv.org/pdf/2305.13773v1.pdf | Understanding Text-driven Motion Synthesis with Keyframe Collaboration via Diffusion Models | The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either (a) semantically compliant but uncontrollable over specific pose details, or (b) even deviates from the provided descriptions, bringing animators with undesired cases. In this paper, we propose DiffKFC, a conditional diffusion model for text-driven motion synthesis with keyframes collaborated. Different from plain text-driven designs, full interaction among texts, keyframes and the rest diffused frames are conducted at training, enabling realistic generation under efficient, collaborative dual-level control: coarse guidance at semantic level, with only few keyframes for direct and fine-grained depiction down to body posture level, to satisfy animator requirements without tedious labor. Specifically, we customize efficient Dilated Mask Attention modules, where only partial valid tokens participate in local-to-global attention, indicated by the dilated keyframe mask. For user flexibility, DiffKFC supports adjustment on importance of fine-grained keyframe control. Experimental results show that our model achieves state-of-the-art performance on text-to-motion datasets HumanML3D and KIT. | ['Jianfeng Lu', 'Weiqing Li', 'Shengxiang Hu', 'Bin Li', 'Huaijiang Sun', 'Xiaoning Sun', 'Dong Wei'] | 2023-05-23 | null | null | null | null | ['motion-synthesis'] | ['computer-vision'] | [-1.74354911e-01 1.49717554e-01 -4.46951296e-03 -6.83691129e-02
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bab2957c-368b-4ef0-97af-00755c5efa1c | five-a-network-you-only-need-9k-parameters | 2305.08824 | null | https://arxiv.org/abs/2305.08824v1 | https://arxiv.org/pdf/2305.08824v1.pdf | Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement | A lightweight underwater image enhancement network is of great significance for resource-constrained platforms, but balancing model size, computational efficiency, and enhancement performance has proven difficult for previous approaches. In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater image enhancement network with only $\sim$ 9k parameters and $\sim$ 0.01s processing time. The FA$^{+}$Net employs a two-stage enhancement structure. The strong prior stage aims to decompose challenging underwater degradations into sub-problems, while the fine-grained stage incorporates multi-branch color enhancement module and pixel attention module to amplify the network's perception of details. To the best of our knowledge, FA$^{+}$Net is the only network with the capability of real-time enhancement of 1080P images. Thorough extensive experiments and comprehensive visual comparison, we show that FA$^{+}$Net outperforms previous approaches by obtaining state-of-the-art performance on multiple datasets while significantly reducing both parameter count and computational complexity. The code is open source at https://github.com/Owen718/FiveAPlus-Network. | ['ErKang Chen', 'Yun Liu', 'Shi Jun', 'Wenhao Chai', 'Sixiang Chen', 'Jinbin Bai', 'Tian Ye', 'Jingxia Jiang'] | 2023-05-15 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 2.43136540e-01 5.22127235e-03 7.12892115e-01 -2.37860009e-01
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cb5937d9-aea5-4e8b-a5ab-40bda044cf3c | a-act-action-anticipation-through-cycle | 2204.00942 | null | https://arxiv.org/abs/2204.00942v1 | https://arxiv.org/pdf/2204.00942v1.pdf | A-ACT: Action Anticipation through Cycle Transformations | While action anticipation has garnered a lot of research interest recently, most of the works focus on anticipating future action directly through observed visual cues only. In this work, we take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms. To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate? Is it by anticipating future actions from past experiences? Or is it by simulating possible scenarios based on cues from the present? A recent study on human psychology explains that, in anticipating an occurrence, the human brain counts on both systems. In this work, we study the impact of each system for the task of action anticipation and introduce a paradigm to integrate them in a learning framework. We believe that intelligent systems designed by leveraging the psychological anticipation models will do a more nuanced job at the task of human action prediction. Furthermore, we introduce cyclic transformation in the temporal dimension in feature and semantic label space to instill the human ability of reasoning of past actions based on the predicted future. Experiments on Epic-Kitchen, Breakfast, and 50Salads dataset demonstrate that the action anticipation model learned using a combination of the two systems along with the cycle transformation performs favorably against various state-of-the-art approaches. | ['Tao Mei', 'Amit K. Roy-Chowdhury', 'Liefeng Bo', 'Jingen Liu', 'Akash Gupta'] | 2022-04-02 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.87469459e-01 3.51924062e-01 -1.41288489e-01 -6.72192574e-01
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a222ae41-57b0-4db0-bfbd-80acccb7daf4 | entity-resolution-in-open-domain | null | null | https://aclanthology.org/2021.naacl-industry.4 | https://aclanthology.org/2021.naacl-industry.4.pdf | Entity Resolution in Open-domain Conversations | In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8{\%} relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model. | ['Dilek Hakkani-Tur', 'Yang Liu', 'Yue Liu', 'Han Wang', 'Akshay Grewal', 'Tiantong Deng', 'Matthew Welch', 'Jiyang Wang', 'Jiangning Chen', 'Mihail Eric', 'Tong Wang', 'Mingyue Shang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['entity-resolution'] | ['natural-language-processing'] | [-1.11929357e-01 5.86266935e-01 -1.56213403e-01 -4.44683701e-01
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75b93b4a-13e4-4bbc-95b4-97150aa4405f | double-doubly-robust-thompson-sampling-for | 2209.06983 | null | https://arxiv.org/abs/2209.06983v2 | https://arxiv.org/pdf/2209.06983v2.pdf | Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits | We propose a novel contextual bandit algorithm for generalized linear rewards with an $\tilde{O}(\sqrt{\kappa^{-1} \phi T})$ regret over $T$ rounds where $\phi$ is the minimum eigenvalue of the covariance of contexts and $\kappa$ is a lower bound of the variance of rewards. In several practical cases where $\phi=O(d)$, our result is the first regret bound for generalized linear model (GLM) bandits with the order $\sqrt{d}$ without relying on the approach of Auer [2002]. We achieve this bound using a novel estimator called double doubly-robust (DDR) estimator, a subclass of doubly-robust (DR) estimator but with a tighter error bound. The approach of Auer [2002] achieves independence by discarding the observed rewards, whereas our algorithm achieves independence considering all contexts using our DDR estimator. We also provide an $O(\kappa^{-1} \phi \log (NT) \log T)$ regret bound for $N$ arms under a probabilistic margin condition. Regret bounds under the margin condition are given by Bastani and Bayati [2020] and Bastani et al. [2021] under the setting that contexts are common to all arms but coefficients are arm-specific. When contexts are different for all arms but coefficients are common, ours is the first regret bound under the margin condition for linear models or GLMs. We conduct empirical studies using synthetic data and real examples, demonstrating the effectiveness of our algorithm. | ['Myunghee Cho Paik', 'Kyungbok Lee', 'Wonyoung Kim'] | 2022-09-15 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.99510321e-01 2.34248698e-01 -8.22451293e-01 -3.67912561e-01
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382b34de-7ca9-4ef8-a295-48aee6ee4f97 | neuro-symbolic-reinforcement-learning-with | 2110.10963 | null | https://arxiv.org/abs/2110.10963v1 | https://arxiv.org/pdf/2110.10963v1.pdf | Neuro-Symbolic Reinforcement Learning with First-Order Logic | Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark. | ['Alexander Gray', 'Asim Munawar', 'Michiaki Tatsubori', 'Don Joven Agravante', 'Akifumi Wachi', 'Ryosuke Kohita', 'Subhajit Chaudhury', 'Masaki Ono', 'Daiki Kimura'] | 2021-10-21 | null | https://aclanthology.org/2021.emnlp-main.283 | https://aclanthology.org/2021.emnlp-main.283.pdf | emnlp-2021-11 | ['text-based-games'] | ['playing-games'] | [ 9.79884192e-02 7.24770606e-01 -2.86653399e-01 -3.73211950e-01
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7e108c15-0144-49bf-9aa1-806f8299efdc | a-weak-self-supervision-with-transition-based | null | null | https://openreview.net/forum?id=zO6zGKsaex | https://openreview.net/pdf?id=zO6zGKsaex | A Weak Self-supervision with Transition-Based Modeling for Reference Resolution | The reference resolution is a task to find the link between an entity and its source action in the same recipe. In this study, we introduce a weak self-supervision method with a transition-based model for reference resolution tasks for recipes, where the aim of the task is to make the syntax of the instructions used for reference resolution with self annotation. The results show that our approach to the problem outperforms the previous unsupervised methods with %8 F1. Especially, our models show %82 accuracies of pronoun, and %85 accuracies for null entity resolution. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['entity-resolution'] | ['natural-language-processing'] | [ 2.82577872e-01 6.28111780e-01 -6.34654820e-01 -5.03221273e-01
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106de69f-ebea-48d5-a134-dba6c1e2a7af | cloud-removal-for-remote-sensing-imagery-via | 2009.13015 | null | https://arxiv.org/abs/2009.13015v2 | https://arxiv.org/pdf/2009.13015v2.pdf | Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network | Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network to solve this task and introduce the spatial attention mechanism into the remote sensing imagery cloud removal task, proposes a model named spatial attention generative adversarial network (SpA GAN), which imitates the human visual mechanism, and recognizes and focuses the cloud area with local-to-global spatial attention, thereby enhancing the information recovery of these areas and generating cloudless images with better quality... | ['Heng Pan'] | 2020-09-28 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.96994174e-01 -4.00674760e-01 3.23179901e-01 1.15145100e-02
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7771b73a-01c4-409d-8eae-fb3e8a381639 | causal-semantic-communication-for-digital | 2304.12502 | null | https://arxiv.org/abs/2304.12502v1 | https://arxiv.org/pdf/2304.12502v1.pdf | Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach | A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints by utilizing AI techniques such as causal reasoning. In this paper, a novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems. The CSC system is posed as an imitation learning (IL) problem, where the transmitter, with access to optimal network control policies using a DT, teaches the receiver using SC over a bandwidth limited wireless channel how to improve its knowledge to perform optimal control actions. The causal structure in the source data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to unseen scenarios. The CSC decoder at the receiver is designed to extract and estimate semantic information while ensuring high semantic reliability. The receiver control policies, semantic decoder, and causal inference are formulated as a bi-level optimization problem within a variational inference framework. This problem is solved using a novel concept called network state models, inspired from world models in generative AI, that faithfully represents the environment dynamics leading to data generation. Simulation results demonstrate that the proposed CSC system outperforms state-of-the-art SC systems by achieving better semantic reliability and reduced semantic representation. | ['Yong Xiao', 'Walid Saad', 'Christo Kurisummoottil Thomas'] | 2023-04-25 | null | null | null | null | ['causal-inference', 'causal-inference', 'edge-computing'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.36262286e-01 8.09308469e-01 -5.42946935e-01 -1.87477350e-01
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9319bb70-2383-4e67-a00c-fe3bb0fe2637 | bidirectional-gaitnet-a-bidirectional | 2306.04161 | null | https://arxiv.org/abs/2306.04161v1 | https://arxiv.org/pdf/2306.04161v1.pdf | Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions | We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a comprehensive, full-body, simulation-ready, musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional GaitNet consists of forward and backward models. The forward model predicts a gait pattern of a person with specific physical conditions, while the backward model estimates the physical conditions of a person when his/her gait pattern is provided. Our simulation-based approach first learns the forward model by distilling the simulation data generated by a state-of-the-art predictive gait simulator and then constructs a Variational Autoencoder (VAE) with the learned forward model as its decoder. Once it is learned its encoder serves as the backward model. We demonstrate our model on a variety of healthy/impaired gaits and validate it in comparison with physical examination data of real patients. | ['Jungdam Won', 'Jehee Lee', 'Moon Seok Park', 'Jungnam Park'] | 2023-06-07 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-3.00997317e-01 1.75176203e-01 -4.14049402e-02 -6.32942915e-02
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f72058c6-bc46-41eb-872a-766501e46fcf | a-framework-for-event-based-computer-vision | 2205.06836 | null | https://arxiv.org/abs/2205.06836v1 | https://arxiv.org/pdf/2205.06836v1.pdf | A Framework for Event-based Computer Vision on a Mobile Device | We present the first publicly available Android framework to stream data from an event camera directly to a mobile phone. Today's mobile devices handle a wider range of workloads than ever before and they incorporate a growing gamut of sensors that make devices smarter, more user friendly and secure. Conventional cameras in particular play a central role in such tasks, but they cannot record continuously, as the amount of redundant information recorded is costly to process. Bio-inspired event cameras on the other hand only record changes in a visual scene and have shown promising low-power applications that specifically suit mobile tasks such as face detection, gesture recognition or gaze tracking. Our prototype device is the first step towards embedding such an event camera into a battery-powered handheld device. The mobile framework allows us to stream events in real-time and opens up the possibilities for always-on and on-demand sensing on mobile phones. To liaise the asynchronous event camera output with synchronous von Neumann hardware, we look at how buffering events and processing them in batches can benefit mobile applications. We evaluate our framework in terms of latency and throughput and show examples of computer vision tasks that involve both event-by-event and pre-trained neural network methods for gesture recognition, aperture robust optical flow and grey-level image reconstruction from events. The code is available at https://github.com/neuromorphic-paris/frog | ['Sio-Hoi Ieng', 'Serge Picaud', 'Gregor Lenz'] | 2022-05-13 | null | null | null | null | ['face-detection', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.59541225e-01 -5.39757073e-01 -8.64439160e-02 -2.34179899e-01
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4ca4a299-6a37-49d3-80a3-f925f9959378 | incrementer-transformer-for-class-incremental | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.pdf | Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class | Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes. Based on the above observations, we propose a new transformer framework for class-incremental semantic segmentation, dubbed Incrementer, which only needs to add new class tokens to the transformer decoder for new-class learning. Based on the Incrementer, we propose a new knowledge distillation scheme that focuses on the distillation in the old-class regions, which reduces the constraints of the old model on the new-class learning, thus improving the plasticity. Moreover, we propose a class deconfusion strategy to alleviate the overfitting to new classes and the confusion of similar classes. Our method is simple and effective, and extensive experiments show that our method outperforms the SOTAs by a large margin (5 15 absolute points boosts on both Pascal VOC and ADE20k). We hope that our Incrementer can serve as a new strong pipeline for class-incremental semantic segmentation. | ['Lanxiao Wang', 'Heqian Qiu', 'Qingbo Wu', 'Fanman Meng', 'Hongliang Li', 'Chao Shang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 3.30175370e-01 3.23225081e-01 -2.63573974e-01 -5.55449903e-01
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3e439039-f968-4369-8ef7-2ab306f097cf | fake-face-detection-via-adaptive-residuals | 2005.04945 | null | https://arxiv.org/abs/2005.04945v2 | https://arxiv.org/pdf/2005.04945v2.pdf | Fake face detection via adaptive manipulation traces extraction network | With the proliferation of face image manipulation (FIM) techniques such as Face2Face and Deepfake, more fake face images are spreading over the internet, which brings serious challenges to public confidence. Face image forgery detection has made considerable progresses in exposing specific FIM, but it is still in scarcity of a robust fake face detector to expose face image forgeries under complex scenarios such as with further compression, blurring, scaling, etc. Due to the relatively fixed structure, convolutional neural network (CNN) tends to learn image content representations. However, CNN should learn subtle manipulation traces for image forensics tasks. Thus, we propose an adaptive manipulation traces extraction network (AMTEN), which serves as pre-processing to suppress image content and highlight manipulation traces. AMTEN exploits an adaptive convolution layer to predict manipulation traces in the image, which are reused in subsequent layers to maximize manipulation artifacts by updating weights during the back-propagation pass. A fake face detector, namely AMTENnet, is constructed by integrating AMTEN with CNN. Experimental results prove that the proposed AMTEN achieves desirable pre-processing. When detecting fake face images generated by various FIM techniques, AMTENnet achieves an average accuracy up to 98.52%, which outperforms the state-of-the-art works. When detecting face images with unknown post-processing operations, the detector also achieves an average accuracy of 95.17%. | ['Gaobo Yang', 'Zhiqing Guo', 'Xingming Sun', 'Jiyou Chen'] | 2020-05-11 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.86233896e-01 -2.35797718e-01 2.05899730e-01 -2.77958632e-01
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790b9cfa-9d43-4d24-877e-09cab19c2668 | confident-neural-network-regression-with | 2202.10903 | null | https://arxiv.org/abs/2202.10903v1 | https://arxiv.org/pdf/2202.10903v1.pdf | Confident Neural Network Regression with Bootstrapped Deep Ensembles | With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. In this paper we present a computationally cheap extension of Deep Ensembles for a regression setting called Bootstrapped Deep Ensembles that explicitly takes the effect of finite data into account using a modified version of the parametric bootstrap. We demonstrate through a simulation study that our method has comparable or better prediction intervals and superior confidence intervals compared to Deep Ensembles and other state-of-the-art methods. As an added bonus, our method is better capable of detecting overfitting than standard Deep Ensembles. | ['Tom Heskes', 'Eric Cator', 'Laurens Sluijterman'] | 2022-02-22 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-5.72307229e-01 -1.30279884e-02 1.22177489e-01 -5.59501648e-01
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13efc19c-acbe-4feb-a6ac-0d67656e13a5 | prolango-protein-function-prediction-using | 1710.07016 | null | http://arxiv.org/abs/1710.07016v1 | http://arxiv.org/pdf/1710.07016v1.pdf | ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network | With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction. | ['Haiqing Jiang', 'Renzhi Cao', 'Leong Chan', 'Colton Freitas', 'Zhangxin Chen', 'Miao Sun'] | 2017-10-19 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.27511406e-01 -2.75682032e-01 -4.88621965e-02 -4.26908195e-01
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efb69c22-1296-4439-b5d6-0e1c48d8eccb | jointly-localizing-and-describing-events-for | 1804.08274 | null | http://arxiv.org/abs/1804.08274v1 | http://arxiv.org/pdf/1804.08274v1.pdf | Jointly Localizing and Describing Events for Dense Video Captioning | Automatically describing a video with natural language is regarded as a
fundamental challenge in computer vision. The problem nevertheless is not
trivial especially when a video contains multiple events to be worthy of
mention, which often happens in real videos. A valid question is how to
temporally localize and then describe events, which is known as "dense video
captioning." In this paper, we present a novel framework for dense video
captioning that unifies the localization of temporal event proposals and
sentence generation of each proposal, by jointly training them in an end-to-end
manner. To combine these two worlds, we integrate a new design, namely
descriptiveness regression, into a single shot detection structure to infer the
descriptive complexity of each detected proposal via sentence generation. This
in turn adjusts the temporal locations of each event proposal. Our model
differs from existing dense video captioning methods since we propose a joint
and global optimization of detection and captioning, and the framework uniquely
capitalizes on an attribute-augmented video captioning architecture. Extensive
experiments are conducted on ActivityNet Captions dataset and our framework
shows clear improvements when compared to the state-of-the-art techniques. More
remarkably, we obtain a new record: METEOR of 12.96% on ActivityNet Captions
official test set. | ['Yingwei Pan', 'Ting Yao', 'Yehao Li', 'Hongyang Chao', 'Tao Mei'] | 2018-04-23 | jointly-localizing-and-describing-events-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Jointly_Localizing_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Jointly_Localizing_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.27858150e-01 -1.12666180e-02 -1.17246374e-01 -3.07934880e-01
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7b49b6cd-9ec3-4680-a9f6-7a04723cacfa | parsimonious-hmms-for-offline-handwritten | 1808.04138 | null | http://arxiv.org/abs/1808.04138v1 | http://arxiv.org/pdf/1808.04138v1.pdf | Parsimonious HMMs for Offline Handwritten Chinese Text Recognition | Recently, hidden Markov models (HMMs) have achieved promising results for
offline handwritten Chinese text recognition. However, due to the large
vocabulary of Chinese characters with each modeled by a uniform and fixed
number of hidden states, a high demand of memory and computation is required.
In this study, to address this issue, we present parsimonious HMMs via the
state tying which can fully utilize the similarities among different Chinese
characters. Two-step algorithm with the data-driven question-set is adopted to
generate the tied-state pool using the likelihood measure. The proposed
parsimonious HMMs with both Gaussian mixture models (GMMs) and deep neural
networks (DNNs) as the emission distributions not only lead to a compact model
but also improve the recognition accuracy via the data sharing for the tied
states and the confusion decreasing among state classes. Tested on ICDAR-2013
competition database, in the best configured case, the new parsimonious DNN-HMM
can yield a relative character error rate (CER) reduction of 6.2%, 25%
reduction of model size and 60% reduction of decoding time over the
conventional DNN-HMM. In the compact setting case of average 1-state HMM, our
parsimonious DNN-HMM significantly outperforms the conventional DNN-HMM with a
relative CER reduction of 35.5%. | ['Zi-Rui Wang', 'Wenchao Wang', 'Jun Du'] | 2018-08-13 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 1.50075987e-01 -3.03540915e-01 -9.45835412e-02 -2.77223349e-01
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fcc294e2-4d79-4708-95a5-90326cf58497 | deformation-flow-based-two-stream-network-for | 2003.05709 | null | https://arxiv.org/abs/2003.05709v2 | https://arxiv.org/pdf/2003.05709v2.pdf | Deformation Flow Based Two-Stream Network for Lip Reading | Lip reading is the task of recognizing the speech content by analyzing movements in the lip region when people are speaking. Observing on the continuity in adjacent frames in the speaking process, and the consistency of the motion patterns among different speakers when they pronounce the same phoneme, we model the lip movements in the speaking process as a sequence of apparent deformations in the lip region. Specifically, we introduce a Deformation Flow Network (DFN) to learn the deformation flow between adjacent frames, which directly captures the motion information within the lip region. The learned deformation flow is then combined with the original grayscale frames with a two-stream network to perform lip reading. Different from previous two-stream networks, we make the two streams learn from each other in the learning process by introducing a bidirectional knowledge distillation loss to train the two branches jointly. Owing to the complementary cues provided by different branches, the two-stream network shows a substantial improvement over using either single branch. A thorough experimental evaluation on two large-scale lip reading benchmarks is presented with detailed analysis. The results accord with our motivation, and show that our method achieves state-of-the-art or comparable performance on these two challenging datasets. | ['Yuan-Hang Zhang', 'Jing-Yun Xiao', 'Xilin Chen', 'Shuang Yang', 'Shiguang Shan'] | 2020-03-12 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.30016291e-01 3.54801752e-02 -6.36851430e-01 -2.71825463e-01
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f8ad1b93-d1fe-4263-b2de-2147505d7195 | mathematics-word-problems-common-sense-and | 2301.09723 | null | https://arxiv.org/abs/2301.09723v2 | https://arxiv.org/pdf/2301.09723v2.pdf | Mathematics, word problems, common sense, and artificial intelligence | The paper discusses the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary knowledge with commonsense reasoning. No existing AI systems can solve these reliably. We review three approaches that have been developed, using AI natural language technology: outputting the answer directly, outputting a computer program that solves the problem, and outputting a formalized representation that can be input to an automated theorem verifier. We review some benchmarks that have been developed to evaluate these systems and some experimental studies. We discuss the limitations of the existing technology at solving these kinds of problems. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research, but that they will be important in applications of mathematics, and may well be important in developing programs capable of reading and understanding mathematical content written by humans. | ['Ernest Davis'] | 2023-01-23 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 3.87731791e-01 3.84759843e-01 6.73139617e-02 -3.29886854e-01
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e917ca48-90fe-4339-9225-7e4e8077b612 | scenesketcher-fine-grained-image-retrieval | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3324_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640698.pdf | SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches | Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrieving scene images satisfying the user's specific requirements via a freehand scene sketch. We propose a graph embedding based method to learn the similarity measurement between images and scene sketches, which models the multi-modal information, including the size and appearance of objects as well as their layout information, in an effective manner. To evaluate our approach, we collect a dataset based on SketchyCOCO and extend the dataset using Coco-stuff. Comprehensive experiments demonstrate the significant potential of the proposed approach on the application of fine-grained scene-level image retrieval. | ['Yong-Jin Liu', 'Yu-Kun Lai', 'Ran Zuo', 'Hongan Wang', 'Cuixia Ma', 'Changqing Zou', 'Xiaoming Deng', 'Fang Liu'] | null | null | null | null | eccv-2020-8 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.04042926e-01 -7.25343049e-01 -1.38762057e-01 -2.91763127e-01
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3e713b87-bf0b-47e7-adc5-5de4864950ed | restoring-and-attributing-ancient-texts-using | null | null | https://www.nature.com/articles/s41586-022-04448-z | https://www.nature.com/articles/s41586-022-04448-z.pdf | Restoring and attributing ancient texts using deep neural networks | Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history. | ['Nando de Freitas', 'Jonathan Prag', 'Ion Androutsopoulos', 'Marita Chatzipanagiotou', 'John Pavlopoulos', 'Mahyar Bordbar', 'Brendan Shillingford', 'Thea Sommerschield', 'Yannis Assael'] | 2022-03-09 | null | null | null | nature-2022-3 | ['ancient-tex-restoration'] | ['miscellaneous'] | [-1.97024465e-01 3.93749446e-01 -2.27064773e-01 7.12629110e-02
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69b9d20e-ce94-4c1e-84fe-855123f44e10 | oriole-thwarting-privacy-against-trustworthy | 2102.11502 | null | https://arxiv.org/abs/2102.11502v2 | https://arxiv.org/pdf/2102.11502v2.pdf | Oriole: Thwarting Privacy against Trustworthy Deep Learning Models | Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of training high-precision face recognition models, creating a serious violation of privacy. Recently, a well-known system named Fawkes (published in USENIX Security 2020) claimed this privacy threat can be neutralized by uploading cloaked user images instead of their original images. In this paper, we present Oriole, a system that combines the advantages of data poisoning attacks and evasion attacks, to thwart the protection offered by Fawkes, by training the attacker face recognition model with multi-cloaked images generated by Oriole. Consequently, the face recognition accuracy of the attack model is maintained and the weaknesses of Fawkes are revealed. Experimental results show that our proposed Oriole system is able to effectively interfere with the performance of the Fawkes system to achieve promising attacking results. Our ablation study highlights multiple principal factors that affect the performance of the Oriole system, including the DSSIM perturbation budget, the ratio of leaked clean user images, and the numbers of multi-cloaks for each uncloaked image. We also identify and discuss at length the vulnerabilities of Fawkes. We hope that the new methodology presented in this paper will inform the security community of a need to design more robust privacy-preserving deep learning models. | ['Haifeng Qian', 'Minhui Xue', 'Benjamin Zi Hao Zhao', 'Hu Wang', 'Liuqiao Chen'] | 2021-02-23 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 3.45250443e-02 8.78595859e-02 1.28664985e-01 -1.82712063e-01
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0686bb6e-18b8-4f59-b009-3edc1cfe19a5 | improved-instruction-ordering-in-recipe | 2305.17280 | null | https://arxiv.org/abs/2305.17280v1 | https://arxiv.org/pdf/2305.17280v1.pdf | Improved Instruction Ordering in Recipe-Grounded Conversation | In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge for a recipe-grounded dialog system is how to provide the instructions in the correct order. We hypothesize that this is due to the model's lack of understanding of user intent and inability to track the instruction state (i.e., which step was last instructed). Therefore, we propose to explore two auxiliary subtasks, namely User Intent Detection and Instruction State Tracking, to support Response Generation with improved instruction grounding. Experimenting with our newly collected dataset, ChattyChef, shows that incorporating user intent and instruction state information helps the response generation model mitigate the incorrect order issue. Furthermore, to investigate whether ChatGPT has completely solved this task, we analyze its outputs and find that it also makes mistakes (10.7% of the responses), about half of which are out-of-order instructions. We will release ChattyChef to facilitate further research in this area at: https://github.com/octaviaguo/ChattyChef. | ['Alan Ritter', 'Wei Xu', 'Ruohao Guo', 'Duong Minh Le'] | 2023-05-26 | null | null | null | null | ['response-generation', 'intent-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.01872258e-01 3.75417590e-01 -3.14251473e-03 -3.92811209e-01
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2e161e18-ca1d-4118-b2dc-403ad760c24f | polyu-bpcoma-a-dataset-and-benchmark-towards | 2206.07468 | null | https://arxiv.org/abs/2206.07468v2 | https://arxiv.org/pdf/2206.07468v2.pdf | PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System | Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in each surveyed area as targets and ground truth data are collected by an advanced terrestrial laser scanner (TLS). 3D geometrical and color information can be recovered in the colorized point clouds produced by the backpack system and the TLS, respectively. Accordingly, we provide an opportunity to benchmark the mapping and colorization accuracy simultaneously for a mobile multisensorial system. The dataset is approximately 800 GB in size covering both indoor and outdoor environments. The dataset and development kits are available at https://github.com/chenpengxin/PolyU-BPCoMa.git. | ['Daping Yang', 'Yue Yu', 'Haodong Xiang', 'Sheng Bao', 'Muyang Wang', 'Pengxin Chen', 'Wenzhong Shi'] | 2022-06-15 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.70837060e-01 -7.23362863e-01 2.76065201e-01 -4.15061891e-01
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5cb4c76d-c56a-436b-b8ff-8333ef6ca345 | three-stream-fusion-network-for-first-person | 2002.08219 | null | https://arxiv.org/abs/2002.08219v1 | https://arxiv.org/pdf/2002.08219v1.pdf | Three-Stream Fusion Network for First-Person Interaction Recognition | First-person interaction recognition is a challenging task because of unstable video conditions resulting from the camera wearer's movement. For human interaction recognition from a first-person viewpoint, this paper proposes a three-stream fusion network with two main parts: three-stream architecture and three-stream correlation fusion. Thre three-stream architecture captures the characteristics of the target appearance, target motion, and camera ego-motion. Meanwhile the three-stream correlation fusion combines the feature map of each of the three streams to consider the correlations among the target appearance, target motion and camera ego-motion. The fused feature vector is robust to the camera movement and compensates for the noise of the camera ego-motion. Short-term intervals are modeled using the fused feature vector, and a long short-term memory(LSTM) model considers the temporal dynamics of the video. We evaluated the proposed method on two-public benchmark datasets to validate the effectiveness of our approach. The experimental results show that the proposed fusion method successfully generated a discriminative feature vector, and our network outperformed all competing activity recognition methods in first-person videos where considerable camera ego-motion occurs. | ['Seong-Whan Lee', 'Dong-Gyu Lee', 'Ye-Ji Kim'] | 2020-02-19 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 2.46734068e-01 -8.50427330e-01 -1.26922637e-01 -1.45092621e-01
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e9a40fe5-4ca4-4954-8896-1c39da63e559 | pairwise-instance-relation-augmentation-for | 2211.10685 | null | https://arxiv.org/abs/2211.10685v1 | https://arxiv.org/pdf/2211.10685v1.pdf | Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification | Multi-label text classification (MLTC) is one of the key tasks in natural language processing. It aims to assign multiple target labels to one document. Due to the uneven popularity of labels, the number of documents per label follows a long-tailed distribution in most cases. It is much more challenging to learn classifiers for data-scarce tail labels than for data-rich head labels. The main reason is that head labels usually have sufficient information, e.g., a large intra-class diversity, while tail labels do not. In response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels. PIRAN consists of a relation collector and an instance generator. The former aims to extract the document pairwise relations from head labels. Taking these relations as perturbations, the latter tries to generate new document instances in high-level feature space around the limited given tailed-label instances. Meanwhile, two regularizers (diversity and consistency) are designed to constrain the generation process. The consistency-regularizer encourages the variance of tail labels to be close to head labels and further balances the whole datasets. And diversity-regularizer makes sure the generated instances have diversity and avoids generating redundant instances. Extensive experimental results on three benchmark datasets demonstrate that PIRAN consistently outperforms the SOTA methods, and dramatically improves the performance of tail labels. | ['Xiangliang Zhang', 'Liping Jing', 'Pengyu Xu', 'Lin Xiao'] | 2022-11-19 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 3.19773197e-01 2.09046677e-01 -5.53136051e-01 -6.61992192e-01
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d5ba5339-c1ed-4fdf-8c3c-80d43ff207a8 | ensemble-multi-quantile-adaptively-flexible | 2211.14545 | null | https://arxiv.org/abs/2211.14545v3 | https://arxiv.org/pdf/2211.14545v3.pdf | Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification | We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$ in regression tasks. This conditional distribution's quantiles of probability levels spreading the interval $(0,1)$ are boosted by additive models which are designed by us with intuitions and interpretability. We seek an adaptive balance between the structural integrity and the flexibility for $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$, while Gaussian assumption results in a lack of flexibility for real data and highly flexible approaches (e.g., estimating the quantiles separately without a distribution structure) inevitably have drawbacks and may not lead to good generalization. This ensemble multi-quantiles approach called EMQ proposed by us is totally data-driven, and can gradually depart from Gaussian and discover the optimal conditional distribution in the boosting. On extensive regression tasks from UCI datasets, we show that EMQ achieves state-of-the-art performance comparing to many recent uncertainty quantification methods. Visualization results further illustrate the necessity and the merits of such an ensemble model. | ['Wenxuan Ma', 'Yonghua Su', 'Xing Yan'] | 2022-11-26 | null | null | null | null | ['additive-models'] | ['methodology'] | [-3.86263609e-01 1.30413055e-01 -1.33621335e-01 -8.33395243e-01
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d6c4ec55-d48d-4223-a3e3-2f34cd4b29eb | structure-based-approach-to-identifying-small | 2111.11558 | null | https://arxiv.org/abs/2111.11558v1 | https://arxiv.org/pdf/2111.11558v1.pdf | Structure-based approach to identifying small sets of driver nodes in biological networks | In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories -- centrality measures, propagation measures, and cycle-based measures. Using each measure every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system towards or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without knowledge of the network's dynamics. | ['Réka Albert', 'Jorge Gómez Tejda Zañudo', 'Eli Newby'] | 2021-11-22 | null | null | null | null | ['feedback-vertex-set-fvs'] | ['graphs'] | [ 2.45390400e-01 2.20743120e-01 -8.56274813e-02 2.45409161e-01
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142fc693-78e8-46f7-8975-f316d6fafb2b | end-to-end-dense-video-grounding-via-parallel | 2109.11265 | null | https://arxiv.org/abs/2109.11265v4 | https://arxiv.org/pdf/2109.11265v4.pdf | End-to-End Dense Video Grounding via Parallel Regression | Video grounding aims to localize the corresponding video moment in an untrimmed video given a language query. Existing methods often address this task in an indirect way, by casting it as a proposal-and-match or fusion-and-detection problem. Solving these surrogate problems often requires sophisticated label assignment during training and hand-crafted removal of near-duplicate results. Meanwhile, existing works typically focus on sparse video grounding with a single sentence as input, which could result in ambiguous localization due to its unclear description. In this paper, we tackle a new problem of dense video grounding, by simultaneously localizing multiple moments with a paragraph as input. From a perspective on video grounding as language conditioned regression, we present an end-to-end parallel decoding paradigm by re-purposing a Transformer-alike architecture (PRVG). The key design in our PRVG is to use languages as queries, and directly regress the moment boundaries based on language-modulated visual representations. Thanks to its simplicity in design, our PRVG framework can be applied in different testing schemes (sparse or dense grounding) and allows for efficient inference without any post-processing technique. In addition, we devise a robust proposal-level attention loss to guide the training of PRVG, which is invariant to moment duration and contributes to model convergence. We perform experiments on two video grounding benchmarks of ActivityNet Captions and TACoS, demonstrating that our PRVG can significantly outperform previous methods. We also perform in-depth studies to investigate the effectiveness of parallel regression paradigm on video grounding. | ['Weilin Huang', 'LiMin Wang', 'Fengyuan Shi'] | 2021-09-23 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 3.30063879e-01 -1.67559758e-01 -4.33104664e-01 -2.97004879e-01
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379338c7-d516-4d76-b9bc-3bd577a99042 | a-survey-on-heterogeneous-face-recognition | 1409.5114 | null | https://arxiv.org/abs/1409.5114v2 | https://arxiv.org/pdf/1409.5114v2.pdf | A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution | Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research. | ['Yi-Zhe Song', 'Xueming Li', 'Timothy Hospedales', 'Shuxin Ouyang'] | 2014-09-17 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 5.03324330e-01 -4.55437094e-01 -5.03511071e-01 -5.90660751e-01
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4a155347-f99c-4503-8522-09893ba0a542 | hyperbox-a-supervised-approach-for-hypernym | 2204.02058 | null | https://arxiv.org/abs/2204.02058v2 | https://arxiv.org/pdf/2204.02058v2.pdf | HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings | Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym discovery. Given an input term, HyperBox retrieves its suitable hypernym from a target corpus. For this task, we use the dataset published for SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of our model on two specific domains of knowledge: medical and music. Experimentally, we show that our model outperforms existing methods on the majority of the evaluation metrics. Moreover, our model generalize well over unseen hypernymy pairs using only a small set of training data. | ['Dr. Apurva Narayan', 'Maulik Parmar'] | 2022-04-05 | null | https://aclanthology.org/2022.lrec-1.652 | https://aclanthology.org/2022.lrec-1.652.pdf | lrec-2022-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 1.43425658e-01 3.11441123e-01 -5.00867069e-01 -9.53415632e-02
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f057a422-8729-4c55-a6dc-0a6a3efd9e7f | efficient-task-oriented-dialogue-systems-with | 2208.07097 | null | https://arxiv.org/abs/2208.07097v2 | https://arxiv.org/pdf/2208.07097v2.pdf | Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task | The adoption of pre-trained language models in task-oriented dialogue systems has resulted in significant enhancements of their text generation abilities. However, these architectures are slow to use because of the large number of trainable parameters and can sometimes fail to generate diverse responses. To address these limitations, we propose two models with auxiliary tasks for response selection - (1) distinguishing distractors from ground truth responses and (2) distinguishing synthetic responses from ground truth labels. They achieve state-of-the-art results on the MultiWOZ 2.1 dataset with combined scores of 107.5 and 108.3 and outperform a baseline with three times more parameters. We publish reproducible code and checkpoints and discuss the effects of applying auxiliary tasks to T5-based architectures. | ['Todor Kolev', 'Radostin Cholakov'] | 2022-08-15 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 3.44460040e-01 3.72112989e-01 -4.48653810e-02 -5.90205729e-01
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95ddba90-eb09-4ece-8d02-f6d6188fd46d | zone-based-federated-learning-for-mobile | 2303.06246 | null | https://arxiv.org/abs/2303.06246v1 | https://arxiv.org/pdf/2303.06246v1.pdf | Zone-based Federated Learning for Mobile Sensing Data | Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy. We propose Zone-based Federated Learning (ZoneFL) to address these requirements. ZoneFL divides the physical space into geographical zones mapped to a mobile-edge-cloud system architecture for good model accuracy and scalability. Each zone has a federated training model, called a zone model, which adapts well to data and behaviors of users in that zone. Benefiting from the FL design, the user data privacy is protected during the ZoneFL training. We propose two novel zone-based federated training algorithms to optimize zone models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical partitions through merging of neighboring zones or splitting of large zones into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes a zone model by incorporating the gradients derived from neighboring zones' data. ZGD uses a self-attention mechanism to dynamically control the impact of one zone on its neighbors. Extensive analysis and experimental results demonstrate that ZoneFL significantly outperforms traditional FL in two models for heart rate prediction and human activity recognition. In addition, we developed a ZoneFL system using Android phones and AWS cloud. The system was used in a heart rate prediction field study with 63 users for 4 months, and we demonstrated the feasibility of ZoneFL in real-life. | ['Cristian Borcea', 'Ruoming Jin', 'An Chen', 'Vijaya Datta Mayyuri', 'Hessamaldin Mohammadi', 'NhatHai Phan', 'Thinh On', 'Xiaopeng Jiang'] | 2023-03-10 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [-1.28997624e-01 -5.91586307e-02 -9.31444943e-01 -4.44237113e-01
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a14ed4a6-0820-4bbc-97db-046b7f400975 | multi-view-subspace-adaptive-learning-via | 2201.00171 | null | https://arxiv.org/abs/2201.00171v1 | https://arxiv.org/pdf/2201.00171v1.pdf | Multi-view Subspace Adaptive Learning via Autoencoder and Attention | Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and Autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets. | ['Xiong-lin Luo', 'Run-kun Lu', 'Hao-jie Xie', 'Jian-wei Liu'] | 2022-01-01 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-5.27308345e-01 -5.81571996e-01 -3.38046789e-01 -3.22193742e-01
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08b70c72-bbfd-46f2-83c4-976e926b0f9b | unisar-a-unified-structure-aware | null | null | https://openreview.net/forum?id=QWqNTJUZzss | https://openreview.net/pdf?id=QWqNTJUZzss | UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL | Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings. We present UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits from directly using an off-the-shelf language model architecture and demonstrates consistently high performance under different settings. Specifically, UniSAr extends existing autoregressive language models to incorporate three non-invasive extensions to make them structure-aware: (1) adding structure mark to encode database schema, conversation context, and their relationships; (2) constrained decoding to decode well structured SQL for a given database schema; and (3) SQL completion to complete potential missing JOIN relationships in SQL based on database schema. On seven well-known text-to-SQL datasets including multi-domain, multi-table and multi-turn, UniSAr demonstrates highly comparable or better performance to the most advanced specifically-designed text-to-SQL models. Importantly, our UniSAr is non-invasive, such that other core model advances in text-to-SQL can also adopt our extensions to further enhance performance. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [-5.17782420e-02 3.03989112e-01 -3.65592420e-01 -9.24207330e-01
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69356342-dbd0-4ac9-8872-99582094e32a | learning-dynamics-and-generalization-in | 2206.02126 | null | https://arxiv.org/abs/2206.02126v1 | https://arxiv.org/pdf/2206.02126v1.pdf | Learning Dynamics and Generalization in Reinforcement Learning | Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations. In this paper, we analyze the learning dynamics of temporal difference algorithms to gain novel insight into the tension between these two objectives. We show theoretically that temporal difference learning encourages agents to fit non-smooth components of the value function early in training, and at the same time induces the second-order effect of discouraging generalization. We corroborate these findings in deep RL agents trained on a range of environments, finding that neural networks trained using temporal difference algorithms on dense reward tasks exhibit weaker generalization between states than randomly initialized networks and networks trained with policy gradient methods. Finally, we investigate how post-training policy distillation may avoid this pitfall, and show that this approach improves generalization to novel environments in the ProcGen suite and improves robustness to input perturbations. | ['Yarin Gal', 'Marta Kwiatkowska', 'Will Dabney', 'Mark Rowland', 'Clare Lyle'] | 2022-06-05 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.75475970e-01 2.02118218e-01 -1.07760049e-01 -5.64570315e-02
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12a046b5-139c-47f4-9db6-9580f1680caf | when-adversarial-attacks-become-interpretable | 2206.06854 | null | https://arxiv.org/abs/2206.06854v2 | https://arxiv.org/pdf/2206.06854v2.pdf | On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective | Input gradients have a pivotal role in a variety of applications, including adversarial attack algorithms for evaluating model robustness, explainable AI techniques for generating Saliency Maps, and counterfactual explanations. However, Saliency Maps generated by traditional neural networks are often noisy and provide limited insights. In this paper, we demonstrate that, on the contrary, the Saliency Maps of 1-Lipschitz neural networks, learnt with the dual loss of an optimal transportation problem, exhibit desirable XAI properties: They are highly concentrated on the essential parts of the image with low noise, significantly outperforming state-of-the-art explanation approaches across various models and metrics. We also prove that these maps align unprecedentedly well with human explanations on ImageNet. To explain the particularly beneficial properties of the Saliency Map for such models, we prove this gradient encodes both the direction of the transportation plan and the direction towards the nearest adversarial attack. Following the gradient down to the decision boundary is no longer considered an adversarial attack, but rather a counterfactual explanation that explicitly transports the input from one class to another. Thus, Learning with such a loss jointly optimizes the classification objective and the alignment of the gradient , i.e. the Saliency Map, to the transportation plan direction. These networks were previously known to be certifiably robust by design, and we demonstrate that they scale well for large problems and models, and are tailored for explainability using a fast and straightforward method. | ['Thibaut Boissin', 'Louis Béthune', 'Thomas Fel', 'Franck Mamalet', 'Mathieu Serrurier'] | 2022-06-14 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.69680929e-01 7.69386053e-01 -3.43023866e-01 -3.52318823e-01
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8c8178ff-e7bf-4fe2-aee7-6e0dc9bd5b5f | robust-graph-representation-learning-via | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | Robust Graph Representation Learning via Neural Sparsification | Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection. Real-life graphs usually have complex information in the local neighborhood, where each node is described by a rich set of features and connects to dozens or even hundreds of neighbors. Despite the success of neighborhood aggregation in graph neural networks, task-irrelevant information is mixed into nodes' neighborhood, making learned models suffer from sub-optimal generalization performance. In this paper, we present NeuralSparse, a supervised graph sparsification technique that improves generalization power by learning to remove potentially task-irrelevant edges from input graphs. Our method takes both structural and non-structural information as input, utilizes deep neural networks to parameterize sparsification processes, and optimizes the parameters by feedback signals from downstream tasks. Under the NeuralSparse framework, supervised graph sparsification could seamlessly connect with existing graph neural networks for more robust performance. Experimental results on both benchmark and private datasets show that NeuralSparse can yield up to 7.2% improvement in testing accuracy when working with existing graph neural networks on node classification tasks. | ['Wei Cheng', 'Dongjin Song', 'Wenchao Yu', 'Bo Zong', 'Jingchao Ni', 'Wei Wang', 'Haifeng Chen', 'Cheng Zheng'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | icml-2020-1 | ['product-recommendation'] | ['miscellaneous'] | [ 2.16293111e-01 5.68971157e-01 -5.66780746e-01 -5.44376314e-01
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a6ebe738-60fe-4e3d-8123-d5caf2d18add | a-sparsity-algorithm-with-applications-to | 2107.10306 | null | https://arxiv.org/abs/2107.10306v1 | https://arxiv.org/pdf/2107.10306v1.pdf | A Sparsity Algorithm with Applications to Corporate Credit Rating | In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the original one. In this work we formulate the problem of finding a counterfactual explanation as an optimization problem. We propose a new "sparsity algorithm" which solves the optimization problem, while also maximizing the sparsity of the counterfactual explanation. We apply the sparsity algorithm to provide a simple suggestion to publicly traded companies in order to improve their credit ratings. We validate the sparsity algorithm with a synthetically generated dataset and we further apply it to quarterly financial statements from companies in financial, healthcare and IT sectors of the US market. We provide evidence that the counterfactual explanation can capture the nature of the real statement features that changed between the current quarter and the following quarter when ratings improved. The empirical results show that the higher the rating of a company the greater the "effort" required to further improve credit rating. | ['Ionut Florescu', 'Zhi Chen', 'Dan Wang'] | 2021-07-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.54578441e-01 7.15684712e-01 -6.08192742e-01 -5.50212622e-01
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ea58c4d7-4397-49a0-9730-a75a8bd0fe61 | a-hitchhikers-guide-on-distributed-training | 1810.11787 | null | http://arxiv.org/abs/1810.11787v1 | http://arxiv.org/pdf/1810.11787v1.pdf | A Hitchhiker's Guide On Distributed Training of Deep Neural Networks | Deep learning has led to tremendous advancements in the field of Artificial
Intelligence. One caveat however is the substantial amount of compute needed to
train these deep learning models. Training a benchmark dataset like ImageNet on
a single machine with a modern GPU can take upto a week, distributing training
on multiple machines has been observed to drastically bring this time down.
Recent work has brought down ImageNet training time to a time as low as 4
minutes by using a cluster of 2048 GPUs. This paper surveys the various
algorithms and techniques used to distribute training and presents the current
state of the art for a modern distributed training framework. More
specifically, we explore the synchronous and asynchronous variants of
distributed Stochastic Gradient Descent, various All Reduce gradient
aggregation strategies and best practices for obtaining higher throughout and
lower latency over a cluster such as mixed precision training, large batch
training and gradient compression. | ['Kuntal Dey', 'Manraj Singh Grover', 'Karanbir Chahal'] | 2018-10-28 | null | null | null | null | ['2048'] | ['playing-games'] | [-3.53599280e-01 -4.44774747e-01 1.96112052e-01 -7.98025548e-01
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f34372bf-27e9-4f36-a251-21331d92be17 | skeleton-based-human-action-evaluation-using | null | null | https://www.sciencedirect.com/science/article/pii/S003132032100282X | https://www.sciencedirect.com/science/article/pii/S003132032100282X | Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression | Human action evaluation (HAE) involves judgments about the abnormality and quality of human actions. If performed effectively, HAE based on skeleton data can be used to monitor the outcomes of behavioral therapies for Alzheimer's disease (AD). In this paper, we propose a two-task graph convolutional network (2T-GCN) to represent skeleton data for HAE tasks involving abnormality detection and quality evaluation. The network is first evaluated using the UI-PRMD dataset and demonstrates accurate abnormality detection. Regarding quality evaluation, in addition to laboratory-collected UI-PRMD data, we test the network on a set of real exercise data collected from patients with AD. A numerical score indicating the degree to which actions deviate from normal is taken to reflect the severity of AD; thus, we apply 2T-GCN to determine such scores. Experimental results show that numerical scores for certain exercises performed by patients with AD are consistent with their AD severity level as identified by clinical staff. This corroboration highlights the potential of our approach for monitoring AD and other neurodegenerative diseases. | ['Xiaoying Wang', 'Qintai Yang', 'Keith C. C. Chan', 'Yan Liu', 'Bruce X. B. Yu'] | 2021-06-21 | null | null | null | pattern-recognition-2021-6 | ['action-assessment'] | ['computer-vision'] | [ 2.92296082e-01 1.68401495e-01 -7.84408599e-02 -4.57993627e-01
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80edc1b9-f11e-4dae-9fc4-adf7b385bdd5 | probabilistic-spatial-analysis-in | 2102.11865 | null | https://arxiv.org/abs/2102.11865v1 | https://arxiv.org/pdf/2102.11865v1.pdf | Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps | 3D microscopy is key in the investigation of diverse biological systems, and the ever increasing availability of large datasets demands automatic cell identification methods that not only are accurate, but also can imply the uncertainty in their predictions to inform about potential errors and hence confidence in conclusions using them. While conventional deep learning methods often yield deterministic results, advances in deep Bayesian learning allow for accurate predictions with a probabilistic interpretation in numerous image classification and segmentation tasks. It is however nontrivial to extend such Bayesian methods to cell detection, which requires specialized learning frameworks. In particular, regression of density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which hinders any meaningful probabilistic output. We herein propose a deep learning-based cell detection framework that can operate on large microscopy images and outputs desired probabilistic predictions by (i) integrating Bayesian techniques for the regression of uncertainty-aware density maps, where peak detection can be applied to generate cell proposals, and (ii) learning a mapping from the numerous proposals to a probabilistic space that is calibrated, i.e. accurately represents the chances of a successful prediction. Utilizing such calibrated predictions, we propose a probabilistic spatial analysis with Monte-Carlo sampling. We demonstrate this in revising an existing description of the distribution of a mesenchymal stromal cell type within the bone marrow, where our proposed methods allow us to reveal spatial patterns that are otherwise undetectable. Introducing such probabilistic analysis in quantitative microscopy pipelines will allow for reporting confidence intervals for testing biological hypotheses of spatial distributions. | ['Orcun Goksel', 'César Nombela-Arrieta', 'Tiziano Portenier', 'Alvaro Gomariz'] | 2021-02-23 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.60538989e-01 -1.21510901e-01 2.29234308e-01 -2.85778254e-01
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9208cc08-0dad-4d0e-923b-574f1dfeeed1 | where-is-my-hand-deep-hand-segmentation-for | 2102.04750 | null | https://arxiv.org/abs/2102.04750v1 | https://arxiv.org/pdf/2102.04750v1.pdf | Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots | The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the intended tasks with dexterity and without colliding with obstacles. They are fundamental for several applications, from Human-Robot Interaction tasks to object manipulation. Modern humanoid robots are characterized by high number of degrees of freedom which makes their forward kinematics models very sensitive to uncertainty. Thus, resorting to vision sensing can be the only solution to endow these robots with a good perception of the self, being able to localize their body parts with precision. In this paper, we propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. It is known that CNNs require a huge amount of data to be trained. To overcome the challenge of labeling real-world images, we propose the use of simulated datasets exploiting domain randomization techniques. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy. We focus our attention on developing a methodology that requires low amounts of data to achieve reasonable performance while giving detailed insight on how to properly generate variability in the training dataset. Moreover, we analyze the fine-tuning process within the complex model of Mask-RCNN, understanding which weights should be transferred to the new task of segmenting robot hands. Our final model was trained solely on synthetic images and achieves an average IoU of 82% on synthetic validation data and 56.3% on real test data. These results were achieved with only 1000 training images and 3 hours of training time using a single GPU. | ['Alexandre Bernardino', 'Pedro Vicente', 'Alexandre Almeida'] | 2021-02-09 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 2.49796346e-01 2.31243044e-01 1.41844690e-01 -1.14078656e-01
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1626a0e1-e255-4289-a9d8-84090e0906f9 | few-shot-single-view-3d-reconstruction-with | 2208.00183 | null | https://arxiv.org/abs/2208.00183v1 | https://arxiv.org/pdf/2208.00183v1.pdf | Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network | 3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D reconstruction framework. With the shape memory, a multi-head attention module is proposed to capture different parts of a candidate shape prior and fuse these parts together to guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware contrastive learning method, which can not only complement the retrieval accuracy of memory network, but also better organize image features for downstream tasks. Compared with previous few-shot 3D reconstruction methods, MPCN can handle the inter-class variability without category annotations. Experimental results on a benchmark synthetic dataset and the Pascal3D+ real-world dataset show that our model outperforms the current state-of-the-art methods significantly. | ['Yu Xiang', 'Xiangdong Zhou', 'Zhixin Ling', 'Yijiang Chen', 'Zhen Xing'] | 2022-07-30 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-9.45197791e-02 -1.55577615e-01 -1.40018180e-01 -5.94823956e-01
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cc8527df-1c15-4add-a2e2-24c6fd6d9af4 | breaking-with-fixed-set-pathology-recognition | 2205.07139 | null | https://arxiv.org/abs/2205.07139v1 | https://arxiv.org/pdf/2205.07139v1.pdf | Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training | When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies outside of their predefined set, thus, making it a necessity to retrain the classifier with additional data when faced with novel classes. In contrast, we investigate direct text supervision to break away from this closed set assumption. By doing so, we avoid noisy label extraction via text classifiers and incorporate more contextual information. We employ a contrastive global-local dual-encoder architecture to learn concepts directly from unstructured medical reports while maintaining its ability to perform free form classification. We investigate relevant properties of open set recognition for radiological data and propose a method to employ currently weakly annotated data into training. We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR, CheXpert, and ChestX-Ray14 for disease classification. We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting. | ['Jens Kleesiek', 'Rainer Stiefelhagen', 'M. Saquib Sarfraz', 'Simon Reiß', 'Constantin Seibold'] | 2022-05-14 | null | null | null | null | ['thoracic-disease-classification', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.83962011e-01 7.93818951e-01 -3.00433874e-01 -6.45365000e-01
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ab5f977b-d281-4175-9946-3edafb5e3798 | grid-anchor-based-image-cropping-a-new | 1909.08989 | null | https://arxiv.org/abs/1909.08989v1 | https://arxiv.org/pdf/1909.08989v1.pdf | Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model | Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available at: \url{https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping-Pytorch}. | ['Hui Zeng', 'Lida Li', 'Lei Zhang', 'Zisheng Cao'] | 2019-09-18 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 1.52211130e-01 -2.83215106e-01 -1.28351510e-01 7.74809495e-02
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6b7ab0de-19f7-4bf7-8f4c-3aafb5b5551c | learning-fine-grained-expressions-to-solve | null | null | https://aclanthology.org/D17-1084 | https://aclanthology.org/D17-1084.pdf | Learning Fine-Grained Expressions to Solve Math Word Problems | This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4{\%} on the linear Dolphin18K benchmark, which is 10{\%} (54{\%} relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12{\%} (59{\%} relative) on the TS6 benchmark subset. | ['Chin-Yew Lin', 'Shuming Shi', 'Jian Yin', 'Danqing Huang'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.42108190e-01 2.06269383e-01 1.08638108e-01 -5.04171968e-01
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b380d8fc-df97-4097-8a5b-6c1eeb4def85 | cross-attentional-audio-visual-fusion-for-1 | 2111.05222 | null | https://arxiv.org/abs/2111.05222v1 | https://arxiv.org/pdf/2111.05222v1.pdf | Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition | Multimodal analysis has recently drawn much interest in affective computing, since it can improve the overall accuracy of emotion recognition over isolated uni-modal approaches. The most effective techniques for multimodal emotion recognition efficiently leverage diverse and complimentary sources of information, such as facial, vocal, and physiological modalities, to provide comprehensive feature representations. In this paper, we focus on dimensional emotion recognition based on the fusion of facial and vocal modalities extracted from videos, where complex spatiotemporal relationships may be captured. Most of the existing fusion techniques rely on recurrent networks or conventional attention mechanisms that do not effectively leverage the complimentary nature of audio-visual (A-V) modalities. We introduce a cross-attentional fusion approach to extract the salient features across A-V modalities, allowing for accurate prediction of continuous values of valence and arousal. Our new cross-attentional A-V fusion model efficiently leverages the inter-modal relationships. In particular, it computes cross-attention weights to focus on the more contributive features across individual modalities, and thereby combine contributive feature representations, which are then fed to fully connected layers for the prediction of valence and arousal. The effectiveness of the proposed approach is validated experimentally on videos from the RECOLA and Fatigue (private) data-sets. Results indicate that our cross-attentional A-V fusion model is a cost-effective approach that outperforms state-of-the-art fusion approaches. Code is available: \url{https://github.com/praveena2j/Cross-Attentional-AV-Fusion} | ['Patrick Cardinal', 'Eric Granger', 'Gnana Praveen R'] | 2021-11-09 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-3.34579162e-02 -5.30305326e-01 -1.33713320e-01 -4.43967253e-01
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71b60706-5d77-43ab-8043-f4c5f653327b | fully-automatic-mitral-valve-4d-shape | 2305.00627 | null | https://arxiv.org/abs/2305.00627v2 | https://arxiv.org/pdf/2305.00627v2.pdf | CNN-based fully automatic mitral valve extraction using CT images and existence probability maps | Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently accurate. In this paper, we propose a fully automated method of extracting mitral valve shape from computed tomography (CT) images for the all phases of the cardiac cycle. This method extracts the mitral valve shape based on DenseNet using both the original CT image and the existence probability maps of the mitral valve area inferred by U-Net as input. A total of 1585 CT images from 204 patients with various cardiac diseases including mitral regurgitation (MR) were collected and manually annotated for mitral valve region. The proposed method was trained and evaluated by 10-fold cross validation using the collected data and was compared with the method without the existence probability maps. The mean error of shape extraction error in the proposed method is 0.88 mm, which is an improvement of 0.32 mm compared with the method without the existence probability maps. | ['Kiyohide Satoh', 'Takuya Sakaguchi', 'Klaus Fuglsang Kofoed', 'Michael Huy Cuong Pham', 'Masahiko Asami', 'James V. Chapman', 'Keitaro Kawashima', 'Gakuto Aoyama', 'Toru Tanaka', 'Ryo Ishikawa', 'Yukiteru Masuda'] | 2023-05-01 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-1.44607276e-01 2.95612305e-01 8.28368142e-02 -3.77366617e-02
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1834cf8c-4fe9-4e64-b06b-10b3d1d22f88 | unsupervised-learning-of-discourse-structures | 2012.09446 | null | https://arxiv.org/abs/2012.09446v1 | https://arxiv.org/pdf/2012.09446v1.pdf | Unsupervised Learning of Discourse Structures using a Tree Autoencoder | Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sophisticated, the growing need for robust and general discourse structures has not been sufficiently met by current discourse parsers, usually trained on small scale datasets in a strictly limited number of domains. This makes the prediction for arbitrary tasks noisy and unreliable. The overall resulting lack of high-quality, high-quantity discourse trees poses a severe limitation to further progress. In order the alleviate this shortcoming, we propose a new strategy to generate tree structures in a task-agnostic, unsupervised fashion by extending a latent tree induction framework with an auto-encoding objective. The proposed approach can be applied to any tree-structured objective, such as syntactic parsing, discourse parsing and others. However, due to the especially difficult annotation process to generate discourse trees, we initially develop a method to generate larger and more diverse discourse treebanks. In this paper we are inferring general tree structures of natural text in multiple domains, showing promising results on a diverse set of tasks. | ['Giuseppe Carenini', 'Patrick Huber'] | 2020-12-17 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.78298569e-01 9.06648517e-01 -1.92257971e-01 -3.94224703e-01
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e9f31be9-9997-46ff-915a-ed32a8a6af6c | inference-for-two-stage-experiments-under | 2301.09016 | null | https://arxiv.org/abs/2301.09016v3 | https://arxiv.org/pdf/2301.09016v3.pdf | Inference for Two-stage Experiments under Covariate-Adaptive Randomization | This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-"average of averages" estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Finally, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application. | ['Jizhou Liu'] | 2023-01-21 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 2.48072580e-01 -5.65244965e-02 -8.24386299e-01 -4.38759476e-01
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1d8abc24-917b-451a-b000-f4f296c767b4 | representation-learning-via-global-temporal | 2105.05217 | null | https://arxiv.org/abs/2105.05217v1 | https://arxiv.org/pdf/2105.05217v1.pdf | Representation Learning via Global Temporal Alignment and Cycle-Consistency | We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval. | ['Allan D. Jepson', 'Konstantinos G. Derpanis', 'Isma Hadji'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-synchronization'] | ['computer-vision'] | [ 3.40950221e-01 -2.28355527e-01 -5.93468130e-01 -3.27435523e-01
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257e8a58-1f14-46e6-94ba-12e3d703e35a | wip-abstract-robust-out-of-distribution | 2107.11736 | null | https://arxiv.org/abs/2107.11736v1 | https://arxiv.org/pdf/2107.11736v1.pdf | WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS | Highly complex deep learning models are increasingly integrated into modern cyber-physical systems (CPS), many of which have strict safety requirements. One problem arising from this is that deep learning lacks interpretability, operating as a black box. The reliability of deep learning is heavily impacted by how well the model training data represents runtime test data, especially when the input space dimension is high as natural images. In response, we propose a robust out-of-distribution (OOD) detection framework. Our approach detects unusual movements from driving video in real-time by combining classical optic flow operation with representation learning via variational autoencoder (VAE). We also design a method to locate OOD factors in images. Evaluation on a driving simulation data set shows that our approach is statistically more robust than related works. | ['Arvind Easwaran', 'Yeli Feng'] | 2021-07-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-9.51652303e-02 -1.32645339e-01 -1.28656358e-01 5.37957251e-03
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1e1622b8-b7e6-41f4-aa57-5af8de57ad9c | symbolic-regression-on-fpgas-for-fast-machine | 2305.04099 | null | https://arxiv.org/abs/2305.04099v1 | https://arxiv.org/pdf/2305.04099v1.pdf | Symbolic Regression on FPGAs for Fast Machine Learning Inference | The high-energy physics community is investigating the feasibility of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to improve physics sensitivity while meeting data processing latency limitations. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches equation space to discover algebraic relations approximating a dataset. We use PySR (software for uncovering these expressions based on evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimise the top metric by pinning the network size because vast hyperparameter space prevents extensive neural architecture search. Conversely, SR selects a set of models on the Pareto front, which allows for optimising the performance-resource tradeoff directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our procedure on a physics benchmark: multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider, and show that we approximate a 3-layer neural network with an inference model that has as low as 5 ns execution time (a reduction by a factor of 13) and over 90% approximation accuracy. | ['Maurizio Pierini', 'Isobel Ojalvo', 'Philip Harris', 'Peter Elmer', 'Sridhara Dasu', 'Miles Cranmer', 'Ekaterina Govorkova', 'Vladimir Loncar', 'Adrian Alan Pol', 'Ho Fung Tsoi'] | 2023-05-06 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.50263429e-01 7.86738768e-02 -2.24813253e-01 -6.11114860e-01
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88938dc1-b7ce-4b8a-8626-67d4fa8b1223 | building-a-data-collection-for-deception | null | null | https://aclanthology.org/W12-0405 | https://aclanthology.org/W12-0405.pdf | Building a Data Collection for Deception Research | null | ['Joan Bachenko', 'Eileen Fitzpatrick'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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31a8e456-a4e0-4379-a142-fc9b90875031 | ad-net-training-a-shadow-detector-with | 1712.01361 | null | http://arxiv.org/abs/1712.01361v2 | http://arxiv.org/pdf/1712.01361v2.pdf | A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation | We propose a novel GAN-based framework for detecting shadows in images, in
which a shadow detection network (D-Net) is trained together with a shadow
attenuation network (A-Net) that generates adversarial training examples. The
A-Net modifies the original training images constrained by a simplified
physical shadow model and is focused on fooling the D-Net's shadow predictions.
Hence, it is effectively augmenting the training data for D-Net with
hard-to-predict cases. The D-Net is trained to predict shadows in both original
images and generated images from the A-Net. Our experimental results show that
the additional training data from A-Net significantly improves the shadow
detection accuracy of D-Net. Our method outperforms the state-of-the-art
methods on the most challenging shadow detection benchmark (SBU) and also
obtains state-of-the-art results on a cross-dataset task, testing on UCF.
Furthermore, the proposed method achieves accurate real-time shadow detection
at 45 frames per second. | ['Dimitris Samaras', 'Minh Hoai', 'Vu Nguyen', 'Tomas F. Yago Vicente', 'Hieu Le'] | 2017-12-04 | ad-net-training-a-shadow-detector-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hieu_Le_AD_Net_Training_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hieu_Le_AD_Net_Training_ECCV_2018_paper.pdf | eccv-2018-9 | ['shadow-detection', 'detecting-shadows'] | ['computer-vision', 'computer-vision'] | [ 8.67328525e-01 5.71569085e-01 3.58568519e-01 -3.27548176e-01
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2d64836f-c775-497b-aa66-7c980b090afc | effective-document-image-enhancement-using | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4354038 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4354038 | Effective Document Image Enhancement Using tokens-to-token Transformer Network | Document image enhancement is a fundamental and important stage for attaining the best performance in any document analysis assignment because there are many degradation situations that could harm document images, making it more difficult to recognize and analyze them. In this paper, we propose to employ a Tokens-to-Token Transformer network for document image enhancement, a novel encoder-decoder architecture based on a tokens-to-token vision transformer. The proposed architecture uses a tokens-to-token architecture in the encoder section. Each image is divided into a set of tokens with a defined length using the ViT model, which is then applied several times to model the global relationship between the tokens. However, the conventional tokenization of input data does not adequately reflect the crucial local structure between adjacent pixels of the input image, which results in low efficiency. Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokeniza-tion technique to capture this local information from an image for achieving more effective results. Experiments on various DIBCO and H-DIBCO benchmarks demonstrate that the proposed model outperforms the existing CNN and ViT-based state-of-the-art methods. In this research, the primary area of examination is the application of the proposed architecture to the task of document binarization. The source code will be made available at https://github.com/RisabBiswas/T2T-BinFormer. | ['Umapada Pal', 'Swalpa Kumar Roy', 'Risab Biswas'] | 2023-02-10 | null | null | null | preprint-2023-2 | ['image-enhancement'] | ['computer-vision'] | [ 4.78673309e-01 -4.82918173e-01 -2.94299182e-02 -2.07976669e-01
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3fa3d444-7908-4808-b2ac-51735e5e0fd7 | almeria-boosting-pairwise-molecular-contrasts | 2305.13254 | null | https://arxiv.org/abs/2305.13254v1 | https://arxiv.org/pdf/2305.13254v1.pdf | ALMERIA: Boosting pairwise molecular contrasts with scalable methods | Searching for potential active compounds in large databases is a necessary step to reduce time and costs in modern drug discovery pipelines. Such virtual screening methods seek to provide predictions that allow the search space to be narrowed down. Although cheminformatics has made great progress in exploiting the potential of available big data, caution is needed to avoid introducing bias and provide useful predictions with new compounds. In this work, we propose the decision-support tool ALMERIA (Advanced Ligand Multiconformational Exploration with Robust Interpretable Artificial Intelligence) for estimating compound similarities and activity prediction based on pairwise molecular contrasts while considering their conformation variability. The methodology covers the entire pipeline from data preparation to model selection and hyperparameter optimization. It has been implemented using scalable software and methods to exploit large volumes of data -- in the order of several terabytes -- , offering a very quick response even for a large batch of queries. The implementation and experiments have been performed in a distributed computer cluster using a benchmark, the public access DUD-E database. In addition to cross-validation, detailed data split criteria have been used to evaluate the models on different data partitions to assess their true generalization ability with new compounds. Experiments show state-of-the-art performance for molecular activity prediction (ROC AUC: $0.99$, $0.96$, $0.87$), proving that the chosen data representation and modeling have good properties to generalize. Molecular conformations -- prediction performance and sensitivity analysis -- have also been evaluated. Finally, an interpretability analysis has been performed using the SHAP method. | ['Pilar M. Ortigosa', 'Horacio Pérez-Sánchez', 'Juana L. Redondo', 'Rafael Mena-Yedra'] | 2023-04-28 | null | null | null | null | ['activity-prediction', 'drug-discovery', 'hyperparameter-optimization', 'activity-prediction'] | ['computer-vision', 'medical', 'methodology', 'time-series'] | [ 1.66279763e-01 -1.07115939e-01 -3.36728245e-01 -3.06044400e-01
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4cbc6a52-b034-4b3a-9a1d-4e0ad729a4e9 | wheat-head-counting-by-estimating-a-density | 2303.10542 | null | https://arxiv.org/abs/2303.10542v1 | https://arxiv.org/pdf/2303.10542v1.pdf | Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks | Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. Assessing the production of wheat spikes can help us measure the grain production. Thus, detecting and characterizing spikes from images of wheat fields is an essential component in a wheat breeding process. In this study, we propose three wheat head counting networks (WHCNet\_1, WHCNet\_2 and WHCNet\_3) to accurately estimate the wheat head count from an individual image and construct high quality density map, which illustrates the distribution of wheat heads in the image. The WHCNets are composed of two major components: a convolutional neural network (CNN) as the front-end for wheat head image feature extraction and a CNN with skip connections for the back-end to generate high-quality density maps. The dataset used in this study is the Global Wheat Head Detection (GWHD) dataset, which is a large, diverse, and well-labelled dataset of wheat images and built by a joint international collaborative effort. We compare our methods with CSRNet, a deep learning method which developed for highly congested scenes understanding and performing accurate count estimation as well as presenting high quality density maps. By taking the advantage of the skip connections between CNN layers, WHCNets integrate features from low CNN layers to high CNN layers, thus, the output density maps have both high spatial resolution and detailed representations of the input images. The experiments showed that our methods outperformed CSRNet in terms of the evaluation metrics, mean absolute error (MAE) and the root mean squared error (RMSE) with smaller model sizes. The code has been deposited on GitHub (\url{https://github.com/hyguozz}). | ['Hongyu Guo'] | 2023-03-19 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-4.70576547e-02 -2.02762589e-01 1.29126728e-01 -3.12050998e-01
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f47f7bf5-50df-48bf-8a10-e93f320e4493 | contextual-attention-for-hand-detection-in | 1904.04882 | null | http://arxiv.org/abs/1904.04882v1 | http://arxiv.org/pdf/1904.04882v1.pdf | Contextual Attention for Hand Detection in the Wild | We present Hand-CNN, a novel convolutional network architecture for detecting
hand masks and predicting hand orientations in unconstrained images. Hand-CNN
extends MaskRCNN with a novel attention mechanism to incorporate contextual
cues in the detection process. This attention mechanism can be implemented as
an efficient network module that captures non-local dependencies between
features. This network module can be inserted at different stages of an object
detection network, and the entire detector can be trained end-to-end.
We also introduce a large-scale annotated hand dataset containing hands in
unconstrained images for training and evaluation. We show that Hand-CNN
outperforms existing methods on several datasets, including our hand detection
benchmark and the publicly available PASCAL VOC human layout challenge. We also
conduct ablation studies on hand detection to show the effectiveness of the
proposed contextual attention module. | ['Yang Wang', 'Zhengwei Wei', 'Minh Hoai', 'Justin Zhang', 'Supreeth Narasimhaswamy'] | 2019-04-09 | contextual-attention-for-hand-detection-in-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Narasimhaswamy_Contextual_Attention_for_Hand_Detection_in_the_Wild_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Narasimhaswamy_Contextual_Attention_for_Hand_Detection_in_the_Wild_ICCV_2019_paper.pdf | iccv-2019-10 | ['hand-detection'] | ['computer-vision'] | [-1.16220146e-01 -3.42534661e-01 -3.01460028e-01 -3.00082207e-01
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b32a7950-8e08-4ec4-ac85-eeb11ef25d75 | meteorologists-and-students-a-resource-for | 1809.02494 | null | http://arxiv.org/abs/1809.02494v1 | http://arxiv.org/pdf/1809.02494v1.pdf | Meteorologists and Students: A resource for language grounding of geographical descriptors | We present a data resource which can be useful for research purposes on
language grounding tasks in the context of geographical referring expression
generation. The resource is composed of two data sets that encompass 25
different geographical descriptors and a set of associated graphical
representations, drawn as polygons on a map by two groups of human subjects:
teenage students and expert meteorologists. | ['Kees Van Deemter', 'Alejandro Ramos-Soto', 'Jose M. Alonso', 'Ehud Reiter', 'Albert Gatt'] | 2018-09-07 | meteorologists-and-students-a-resource-for-1 | https://aclanthology.org/W18-6551 | https://aclanthology.org/W18-6551.pdf | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [-1.84754923e-03 6.04569852e-01 -1.39027566e-01 -4.65616673e-01
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93cbf800-2f5e-4323-bae4-86914604befe | unsupervised-temporal-feature-aggregation-for | 2002.08097 | null | https://arxiv.org/abs/2002.08097v1 | https://arxiv.org/pdf/2002.08097v1.pdf | Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos | Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structured television broadcast video datasets with a straight and fixed camera having minimum variability in the capturing pose. In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles. The transition from structured to unstructured video analysis produces multiple challenges that we address in our paper. Specifically, we identify and solve two major problems: unsupervised identification of players in an unstructured setting and generalization of the trained models to pose variations due to arbitrary shooting angles. For the first problem, we propose a temporal feature aggregation algorithm using person re-identification features to obtain high player retrieval precision by boosting a weak heuristic scoring method. Additionally, we propose a data augmentation technique, based on multi-modal image translation model, to reduce bias in the appearance of training samples. Experimental evaluations show that our proposed method improves precision for player retrieval from 0.78 to 0.86 for obliquely angled videos. Additionally, we obtain an improvement in F1 score for rally detection in table tennis videos from 0.79 in case of global frame-level features to 0.89 using our proposed player-level features. Please see the supplementary video submission at https://ibm.biz/BdzeZA. | ['Phongtharin Vinayavekhin', 'Asim Munawar', 'Koji Ito', 'Yuki Inaba', 'Subhajit Chaudhury', 'Shuji Kidokoro', 'Daiki Kimura', 'Ryuki Tachibana', 'Minoru Matsumoto', 'Hiroki Ozaki'] | 2020-02-19 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 2.59722114e-01 -2.88437068e-01 1.29815715e-03 -9.15420875e-02
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8a018863-e248-4f10-84c9-24b7e356be6a | meta-learning-for-code-summarization | null | null | https://openreview.net/forum?id=Sm8-5PzKW0 | https://openreview.net/pdf?id=Sm8-5PzKW0 | Meta Learning for Code Summarization | Source code summarization is the task of generating a high-level natural language description for a segment of programming language
code. Current neural models for the task differ in their architecture and the aspects of code they consider. In this paper, we show that three
SOTA models for code summarization work well on largely disjoint subsets of a large code-base. This complementarity motivates model
combination: We propose three meta-models that select the best candidate summary for a given code segment. The two neural models improve significantly over the performance of the best individual model, obtaining an improvement of 2.1 BLEU points on a dataset of
code segments where at least one of the individual models obtains a non-zero BLEU. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-summarization'] | ['computer-code'] | [ 3.15919340e-01 5.75180531e-01 -8.04494321e-01 -3.09689254e-01
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7b74cd77-753f-41f7-984e-094217ece2fc | 3d-fcn-feature-driven-regression-forest-based | 1806.03019 | null | http://arxiv.org/abs/1806.03019v1 | http://arxiv.org/pdf/1806.03019v1.pdf | 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation | This paper presents a fully automated atlas-based pancreas segmentation
method from CT volumes utilizing 3D fully convolutional network (FCN)
feature-based pancreas localization. Segmentation of the pancreas is difficult
because it has larger inter-patient spatial variations than other organs.
Previous pancreas segmentation methods failed to deal with such variations. We
propose a fully automated pancreas segmentation method that contains novel
localization and segmentation. Since the pancreas neighbors many other organs,
its position and size are strongly related to the positions of the surrounding
organs. We estimate the position and the size of the pancreas (localized) from
global features by regression forests. As global features, we use intensity
differences and 3D FCN deep learned features, which include automatically
extracted essential features for segmentation. We chose 3D FCN features from a
trained 3D U-Net, which is trained to perform multi-organ segmentation. The
global features include both the pancreas and surrounding organ information.
After localization, a patient-specific probabilistic atlas-based pancreas
segmentation is performed. In evaluation results with 146 CT volumes, we
achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap. | ['Kensaku MORI', 'Daniel Rueckert', "Ken'ichi Karasawa", 'Takayuki Kitasaka', 'Michitaka Fujiwara', 'Holger R. Roth', 'Masahiro Oda', 'Kazunari Misawa', 'Natsuki Shimizu'] | 2018-06-08 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [-6.17192626e-01 6.96275532e-02 -2.08871588e-01 -5.85268497e-01
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2bba509b-2424-4ebb-9ee1-8553fe7bd5d4 | rafare-learning-robust-and-accurate-non | 2302.05486 | null | https://arxiv.org/abs/2302.05486v1 | https://arxiv.org/pdf/2302.05486v1.pdf | RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs | We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database; 2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D\&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at http://github.com/zhuhao-nju/rafare . | ['Xun Cao', 'Menghua Wu', 'Yuanxun Lu', 'Hao Zhu', 'Longwei Guo'] | 2023-02-10 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.20870791e-01 -9.86199677e-02 1.17381297e-01 -7.46965170e-01
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4a0a4cab-24ac-4072-83b4-67e3e3f9a55b | region-aware-video-object-segmentation-with | 2207.10258 | null | https://arxiv.org/abs/2207.10258v1 | https://arxiv.org/pdf/2207.10258v1.pdf | Region Aware Video Object Segmentation with Deep Motion Modeling | Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory. This introduces significant redundant computations. To reduce redundancy, we present a Region Aware Video Object Segmentation (RAVOS) approach that predicts regions of interest (ROIs) for efficient object segmentation and memory storage. RAVOS includes a fast object motion tracker to predict their ROIs in the next frame. For efficient segmentation, object features are extracted according to the ROIs, and an object decoder is designed for object-level segmentation. For efficient memory storage, we propose motion path memory to filter out redundant context by memorizing the features within the motion path of objects between two frames. Besides RAVOS, we also propose a large-scale dataset, dubbed OVOS, to benchmark the performance of VOS models under occlusions. Evaluation on DAVIS and YouTube-VOS benchmarks and our new OVOS dataset show that our method achieves state-of-the-art performance with significantly faster inference time, e.g., 86.1 J&F at 42 FPS on DAVIS and 84.4 J&F at 23 FPS on YouTube-VOS. | ['Ajmal Mian', 'Yongsheng Gao', 'Mohammed Bennamoun', 'Bo Miao'] | 2022-07-21 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 2.74319369e-02 -2.05915093e-01 -6.92364573e-01 -4.35124606e-01
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281c650b-b52e-4904-931f-35f016cbd124 | semantic-graph-based-place-recognition-for-3d | 2008.11459 | null | https://arxiv.org/abs/2008.11459v1 | https://arxiv.org/pdf/2008.11459v1.pdf | Semantic Graph Based Place Recognition for 3D Point Clouds | Due to the difficulty in generating the effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most of the existing methods that focus on extracting local, global, and statistical features of raw point clouds, our method aims at the semantic level that can be superior in terms of robustness to environmental changes. Inspired by the perspective of humans, who recognize scenes through identifying semantic objects and capturing their relations, this paper presents a novel semantic graph based approach for place recognition. First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud. Thus, place recognition is modeled as a graph matching problem. Then we design a fast and effective graph similarity network to compute the similarity. Exhaustive evaluations on the KITTI dataset show that our approach is robust to the occlusion as well as viewpoint changes and outperforms the state-of-the-art methods with a large margin. Our code is available at: \url{https://github.com/kxhit/SG_PR}. | ['Yong liu', 'Xiangrui Zhao', 'Feng Wen', 'Xuemeng Yang', 'Xianfang Zeng', 'Mengmeng Wang', 'Guangyao Zhai', 'Xin Kong', 'Wanlong Li'] | 2020-08-26 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.02960296e-01 -3.12288612e-01 -1.13770589e-01 -3.98848057e-01
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14b2ca36-bd96-4620-bbb9-eeb47517966a | self-monitoring-navigation-agent-via | 1901.03035 | null | http://arxiv.org/abs/1901.03035v1 | http://arxiv.org/pdf/1901.03035v1.pdf | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | The Vision-and-Language Navigation (VLN) task entails an agent following
navigational instruction in photo-realistic unknown environments. This
challenging task demands that the agent be aware of which instruction was
completed, which instruction is needed next, which way to go, and its
navigation progress towards the goal. In this paper, we introduce a
self-monitoring agent with two complementary components: (1) visual-textual
co-grounding module to locate the instruction completed in the past, the
instruction required for the next action, and the next moving direction from
surrounding images and (2) progress monitor to ensure the grounded instruction
correctly reflects the navigation progress. We test our self-monitoring agent
on a standard benchmark and analyze our proposed approach through a series of
ablation studies that elucidate the contributions of the primary components.
Using our proposed method, we set the new state of the art by a significant
margin (8% absolute increase in success rate on the unseen test set). Code is
available at https://github.com/chihyaoma/selfmonitoring-agent . | ['Ghassan AlRegib', 'Chih-Yao Ma', 'Richard Socher', 'Zsolt Kira', 'Jiasen Lu', 'Caiming Xiong', 'Zuxuan Wu'] | 2019-01-10 | self-monitoring-navigation-agent-via-1 | https://openreview.net/forum?id=r1GAsjC5Fm | https://openreview.net/pdf?id=r1GAsjC5Fm | iclr-2019-5 | ['vision-language-navigation', 'natural-language-visual-grounding'] | ['computer-vision', 'reasoning'] | [ 2.12377042e-01 -3.76645513e-02 -8.13731477e-02 -2.70465076e-01
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c94c69e7-d5d4-45c4-b8dd-ca517b97c11c | is-robustbench-autoattack-a-suitable | 2112.01601 | null | https://arxiv.org/abs/2112.01601v2 | https://arxiv.org/pdf/2112.01601v2.pdf | Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? | Recently, RobustBench (Croce et al. 2020) has become a widely recognized benchmark for the adversarial robustness of image classification networks. In its most commonly reported sub-task, RobustBench evaluates and ranks the adversarial robustness of trained neural networks on CIFAR10 under AutoAttack (Croce and Hein 2020b) with l-inf perturbations limited to eps = 8/255. With leading scores of the currently best performing models of around 60% of the baseline, it is fair to characterize this benchmark to be quite challenging. Despite its general acceptance in recent literature, we aim to foster discussion about the suitability of RobustBench as a key indicator for robustness which could be generalized to practical applications. Our line of argumentation against this is two-fold and supported by excessive experiments presented in this paper: We argue that I) the alternation of data by AutoAttack with l-inf, eps = 8/255 is unrealistically strong, resulting in close to perfect detection rates of adversarial samples even by simple detection algorithms and human observers. We also show that other attack methods are much harder to detect while achieving similar success rates. II) That results on low-resolution data sets like CIFAR10 do not generalize well to higher resolution images as gradient-based attacks appear to become even more detectable with increasing resolutions. | ['Janis Keuper', 'Margret Keuper', 'Dominik Strassel', 'Peter Lorenz'] | 2021-12-02 | null | https://openreview.net/forum?id=aLB3FaqoMBs | https://openreview.net/pdf?id=aLB3FaqoMBs | aaai-workshop-advml-2022-2 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 1.58625066e-01 1.52593926e-01 2.49595895e-01 3.23222913e-02
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82524d6a-0696-4f46-b2bc-2dc44d0fc15a | crossloc3d-aerial-ground-cross-source-3d | 2303.17778 | null | https://arxiv.org/abs/2303.17778v1 | https://arxiv.org/pdf/2303.17778v1.pdf | CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition | We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting. Cross-source point cloud data corresponds to point sets captured by depth sensors with different accuracies or from different distances and perspectives. We address the challenges in terms of developing 3D place recognition methods that account for the representation gap between points captured by different sources. Our method handles cross-source data by utilizing multi-grained features and selecting convolution kernel sizes that correspond to most prominent features. Inspired by the diffusion models, our method uses a novel iterative refinement process that gradually shifts the embedding spaces from different sources to a single canonical space for better metric learning. In addition, we present CS-Campus3D, the first 3D aerial-ground cross-source dataset consisting of point cloud data from both aerial and ground LiDAR scans. The point clouds in CS-Campus3D have representation gaps and other features like different views, point densities, and noise patterns. We show that our CrossLoc3D algorithm can achieve an improvement of 4.74% - 15.37% in terms of the top 1 average recall on our CS-Campus3D benchmark and achieves performance comparable to state-of-the-art 3D place recognition method on the Oxford RobotCar. We will release the code and CS-Campus3D benchmark. | ['Dinesh Manocha', 'Damon Conover', 'Adarsh Jagan Sathyamoorthy', 'Jing Liang', 'Xijun Wang', 'Montana Hoover', 'Aswath Muthuselvam', 'Tianrui Guan'] | 2023-03-31 | null | null | null | null | ['metric-learning', '3d-place-recognition', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-2.68793643e-01 -4.22643363e-01 7.00454647e-03 -2.28430778e-01
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41d89ac2-34c0-4853-aff7-47e1feef0946 | explosive-proofs-of-mathematical-truths | 2004.00055 | null | https://arxiv.org/abs/2004.00055v2 | https://arxiv.org/pdf/2004.00055v2.pdf | Epistemic Phase Transitions in Mathematical Proofs | Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record. Their very explicitness, however, leads to a paradox, because the probability of error grows exponentially as the argument expands. When a mathematician encounters a proof, how does she come to believe it? Here we show that, under a cognitively-plausible belief formation mechanism combining deductive and abductive reasoning, belief in mathematical arguments can undergo what we call an epistemic phase transition: a dramatic and rapidly-propagating jump from uncertainty to near-complete confidence at reasonable levels of claim-to-claim error rates. To show this, we analyze an unusual dataset of forty-eight machine-aided proofs from the formalized reasoning system Coq, including major theorems ranging from ancient to 21st Century mathematics, along with five hand-constructed cases including Euclid, Apollonius, Hernstein's Topics in Algebra, and Andrew Wiles's proof of Fermat's Last Theorem. Our results bear both on recent work in the history and philosophy of mathematics on how we understand proofs, and on a question, basic to cognitive science, of how we justify complex beliefs. | ['Scott Viteri', 'Simon DeDeo'] | 2020-03-31 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 7.85316676e-02 8.31404328e-01 1.10109514e-02 -1.15793474e-01
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5d0310e4-c81a-456b-a16d-1a6aa58ac09b | catena-causal-and-temporal-relation | null | null | https://aclanthology.org/C16-1007 | https://aclanthology.org/C16-1007.pdf | CATENA: CAusal and TEmporal relation extraction from NAtural language texts | We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machine-learned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts. | ['Paramita Mirza', 'Sara Tonelli'] | 2016-12-01 | catena-causal-and-temporal-relation-1 | https://aclanthology.org/C16-1007 | https://aclanthology.org/C16-1007.pdf | coling-2016-12 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-3.22437406e-01 4.25836712e-01 -7.11868644e-01 -3.33575249e-01
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7aa28665-ec88-4b25-87d0-f6534a3c1904 | a-lossless-intra-reference-block | 2104.01846 | null | https://arxiv.org/abs/2104.01846v1 | https://arxiv.org/pdf/2104.01846v1.pdf | A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC | The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption. This problem is more intensive by the adoption of intra block copy (IBC), a new coding tool in the screen content coding (SCC) extension to High Efficiency Video Coding (HEVC). In this paper, we propose a lossless recompression scheme that compresses the reference blocks in intra prediction, i.e., intra block copy, before storing them into DRAM to alleviate this problem. The proposal performs pixel-wise texture analysis with an edge-based adaptive prediction method yet no signaling for direction in bitstreams, thus achieves a high gain for compression. Experimental results demonstrate that the proposed scheme shows a 72% data reduction rate on average, which solves the memory bandwidth problem. | ['Fan Liang', 'Ren Mao', 'Jian Cao', 'Guangyu Zhong', 'Jun Wang', 'Jiyuan Hu'] | 2021-04-05 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.9655420e-01 -1.5377097e-01 -5.2636796e-01 8.1991352e-02
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b0c531cf-c668-4803-865a-27e0e708d0d1 | towards-fine-dining-recipe-generation-with | 2209.12774 | null | https://arxiv.org/abs/2209.12774v1 | https://arxiv.org/pdf/2209.12774v1.pdf | Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers | Food is essential to human survival. So much so that we have developed different recipes to suit our taste needs. In this work, we propose a novel way of creating new, fine-dining recipes from scratch using Transformers, specifically auto-regressive language models. Given a small dataset of food recipes, we try to train models to identify cooking techniques, propose novel recipes, and test the power of fine-tuning with minimal data. | ['Konstantinos Skianis', 'Konstantinos Katserelis'] | 2022-09-26 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [-4.17102784e-01 -2.61345744e-01 -1.53030530e-02 -5.25600255e-01
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3209b32c-aa5c-4076-bbd4-8d704e93e75d | false-target-detection-in-ofdm-based-joint | 2303.14782 | null | https://arxiv.org/abs/2303.14782v1 | https://arxiv.org/pdf/2303.14782v1.pdf | False Target Detection in OFDM-based Joint RADAR-Communication Systems | Joint RADAR communication (JRC) systems that use orthogonal frequency division multiplexing (OFDM) can be compromised by an adversary that re-produces the received OFDM signal creating thus false RADAR targets. This paper presents a set of algorithms that can be deployed at the JRC system and can detect the presence of false targets. The presence of a false target is detected depending on whether there is residual carrier frequency offset (CFO) beyond Doppler in the received signal, with a Generalized Likelihood Ratio Test (GLRT). To evaluate the performance of our approach we measure the detection probability versus the false alarm rate through simulation for different system configurations of an IEEE 802.11-based JRC system. | ['Antonios Argyriou'] | 2023-03-26 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 6.77792370e-01 -2.33151894e-02 1.50755167e-01 -9.14913788e-02
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80b3f692-b8c7-4369-87fc-0461254042a9 | modeling-time-series-and-spatial-data-for | 2212.13259 | null | https://arxiv.org/abs/2212.13259v1 | https://arxiv.org/pdf/2212.13259v1.pdf | Modeling Time-Series and Spatial Data for Recommendations and Other Applications | With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications. | ['Vinayak Gupta'] | 2022-12-25 | null | null | null | null | ['activity-prediction', 'point-processes', 'temporal-sequences', 'activity-prediction'] | ['computer-vision', 'methodology', 'reasoning', 'time-series'] | [ 4.26794402e-02 -1.55335620e-01 -2.29152724e-01 -1.31732553e-01
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2405e473-6697-4f71-b514-5fac095ac801 | diabetic-retinopathy-grading-system-based-on | 2012.12515 | null | https://arxiv.org/abs/2012.12515v1 | https://arxiv.org/pdf/2012.12515v1.pdf | Diabetic Retinopathy Grading System Based on Transfer Learning | Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD systems based on deep learning (DL) methods have been adopted to get deep learning merits in diagnosing the pathological abnormalities of DR disease. In this paper, we present a full based-DL CAD system depending on multi-label classification. In the proposed DL CAD system, we present a customized efficientNet model in order to diagnose the early and advanced grades of the DR disease. Learning transfer is very useful in training small datasets. We utilized IDRiD dataset. It is a multi-label dataset. The experiments manifest that the proposed DL CAD system is robust, reliable, and deigns promising results in detecting and grading DR. The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity coefficient (DSC) equals 78.45. | ['Mohammed Elmogy', 'Sherif Barakat', 'Eman AbdelMaksoud'] | 2020-12-23 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-2.10340112e-01 -3.94504726e-01 -1.67621933e-02 -7.69610941e-01
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eabce8b2-3504-48a6-a38e-aac5a1ce7040 | non-aligned-supervision-for-real-image | 2303.04940 | null | https://arxiv.org/abs/2303.04940v3 | https://arxiv.org/pdf/2303.04940v3.pdf | Non-aligned supervision for Real Image Dehazing | Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in misaligned hazy and clear image pairs. In this paper, we propose a non-aligned supervision framework that consists of three networks - dehazing, airlight, and transmission. In particular, we explore a non-alignment setting by utilizing a clear reference image that is not aligned with the hazy input image to supervise the dehazing network through a multi-scale reference loss that compares the features of the two images. Our setting makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which was captured using mobile phones in both rural and urban areas. Additionally, we present a mean and variance self-attention network to model the infinite airlight using dark channel prior as position guidance, and employ a channel attention network to estimate the three-channel transmission. Experimental results show that our framework outperforms current state-of-the-art methods in the real-world image dehazing. Phone-Hazy and code will be available at https://github.com/hello2377/NSDNet. | ['Jian Yang', 'Jun Li', 'Xiang Li', 'Jianjun Qian', 'Fei Guo', 'Junkai Fan'] | 2023-03-08 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 4.51605767e-01 -1.74641147e-01 5.71906805e-01 -4.35664505e-01
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0499ff77-da4a-46dd-b00e-20322b28a042 | contfv-a-contrastive-learning-framework-for | null | null | https://openreview.net/forum?id=SMEv6k1Nb2 | https://openreview.net/pdf?id=SMEv6k1Nb2 | ConTFV: A Contrastive Learning Framework for Table-based Fact Verification | Table-based fact verification is a binary classification task where the challenging part lies in the table's structural parsing and symbolic reasoning. Jointly pre-training on abundant textual and tabular data has been conducted for table semantic parsing recently. However, these models are designed for the table's general understanding, directly fine-tuning for table-based fact verification cannot exploit the advantages to the full. In this paper, we propose ConTFV, a Contrastive learning framework for Table-based Fact Verification, to make the pre-trained model more task-relevant and tap its potential for better representations. By transforming it into a semantic similarity task, our method can outperform baselines with 1.2% on TabFact. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 1.60495818e-01 7.81367958e-01 -9.43911970e-01 -5.03995478e-01
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127cca7a-06f0-4974-a915-f92a855968d0 | relphormer-relational-graph-transformer-for | 2205.10852 | null | https://arxiv.org/abs/2205.10852v5 | https://arxiv.org/pdf/2205.10852v5.pdf | Relphormer: Relational Graph Transformer for Knowledge Graph Representations | Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transformer architectures struggle to capture the intrinsically heterogeneous structural and semantic information of knowledge graphs. To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the semantic information within entities and relations. Moreover, we utilize masked knowledge modeling for general knowledge graph representation learning, which can be applied to various KG-based tasks including knowledge graph completion, question answering, and recommendation. Experimental results on six datasets show that Relphormer can obtain better performance compared with baselines. Code is available in https://github.com/zjunlp/Relphormer. | ['Huajun Chen', 'Wei Guo', 'Qiang Chen', 'Jing Chen', 'Feiyu Xiong', 'Xiaozhuan Liang', 'Ningyu Zhang', 'Siyuan Cheng', 'Zhen Bi'] | 2022-05-22 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.70522141e-02 2.79983699e-01 -4.78117377e-01 -3.52589399e-01
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ff549c35-3d2b-4cd3-8f57-d4de243a171d | cips-3d-a-3d-aware-generator-of-gans-based-on | 2110.09788 | null | https://arxiv.org/abs/2110.09788v1 | https://arxiv.org/pdf/2110.09788v1.pdf | CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis | The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The recently proposed NeRF-based GANs made great progress towards 3D-aware generators, but they are unable to generate high-quality images yet. This paper presents CIPS-3D, a style-based, 3D-aware generator that is composed of a shallow NeRF network and a deep implicit neural representation (INR) network. The generator synthesizes each pixel value independently without any spatial convolution or upsampling operation. In addition, we diagnose the problem of mirror symmetry that implies a suboptimal solution and solve it by introducing an auxiliary discriminator. Trained on raw, single-view images, CIPS-3D sets new records for 3D-aware image synthesis with an impressive FID of 6.97 for images at the $256\times256$ resolution on FFHQ. We also demonstrate several interesting directions for CIPS-3D such as transfer learning and 3D-aware face stylization. The synthesis results are best viewed as videos, so we recommend the readers to check our github project at https://github.com/PeterouZh/CIPS-3D | ['Qi Tian', 'Bingbing Ni', 'Lingxi Xie', 'Peng Zhou'] | 2021-10-19 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.34667838e-01 4.29731131e-01 8.40696096e-02 -2.56504536e-01
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d1f3c7de-e27a-40a1-9254-497dc0c69f71 | what-matters-in-training-a-gpt4-style | 2307.02469 | null | https://arxiv.org/abs/2307.02469v1 | https://arxiv.org/pdf/2307.02469v1.pdf | What Matters in Training a GPT4-Style Language Model with Multimodal Inputs? | Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field. To address this issue, this paper presents a systematic and comprehensive study, quantitatively and qualitatively, on training such models. We implement over 20 variants with controlled settings. Concretely, for network structures, we compare different LLM backbones and model designs. For training data, we investigate the impact of data and sampling strategies. For instructions, we explore the influence of diversified prompts on the instruction-following ability of the trained models. For benchmarks, we contribute the first, to our best knowledge, comprehensive evaluation set including both image and video tasks through crowd-sourcing. Based on our findings, we present Lynx, which performs the most accurate multi-modal understanding while keeping the best multi-modal generation ability compared to existing open-sourced GPT4-style models. | ['Tao Kong', 'Yuchen Zhang', 'Yang Wei', 'Guoqiang Wei', 'Jiangnan Xia', 'Jiani Zheng', 'Hanbo Zhang', 'Yan Zeng'] | 2023-07-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.17636539e-01 1.74219280e-01 -3.35116744e-01 -3.10731173e-01
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00c399e3-e2ff-45f6-bcbe-0d648e94a23e | boun-isik-participation-an-unsupervised | null | null | https://aclanthology.org/D19-5722 | https://aclanthology.org/D19-5722.pdf | BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes | This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data. | ['Arzucan {\\"O}zg{\\"u}r', '{\\"O}mer Faruk Tuna', '{\\.I}lknur Karadeniz'] | 2019-11-01 | null | null | null | ws-2019-11 | ['medical-concept-normalization'] | ['medical'] | [ 1.71830013e-01 3.53430450e-01 1.06479116e-01 -3.87839198e-01
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e2ec10ff-5711-46dc-bc8a-400475d68e3a | sriubc-simple-similarity-features-for | null | null | https://aclanthology.org/S12-1091 | https://aclanthology.org/S12-1091.pdf | SRIUBC: Simple Similarity Features for Semantic Textual Similarity | null | ['Eneko Agirre', 'Eric Yeh'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['video-description'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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