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57df171a-3ec6-41a2-9a22-1a031a1d6053 | complex-query-answering-on-eventuality | 2305.19068 | null | https://arxiv.org/abs/2305.19068v1 | https://arxiv.org/pdf/2305.19068v1.pdf | Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints | Querying incomplete knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to make logical inferences about events, states, and activities (i.e., eventualities or situations) to push learning systems from System I to System II, as proposed by Yoshua Bengio. Querying logically from an EVentuality-centric KG (EVKG) can naturally provide references to such kind of intuitive and logical inference. Thus, in this paper, we propose a new framework to leverage neural methods to answer complex logical queries based on an EVKG, which can satisfy not only traditional first-order logic constraints but also implicit logical constraints over eventualities concerning their occurrences and orders. For instance, if we know that ``Food is bad'' happens before ``PersonX adds soy sauce,'' then ``PersonX adds soy sauce'' is unlikely to be the cause of ``Food is bad'' due to implicit temporal constraint. To facilitate consistent reasoning on EVKGs, we propose Complex Eventuality Query Answering (CEQA), a more rigorous definition of CQA that considers the implicit logical constraints governing the temporal order and occurrence of eventualities. In this manner, we propose to leverage theorem provers for constructing benchmark datasets to ensure the answers satisfy implicit logical constraints. We also propose a Memory-Enhanced Query Encoding (MEQE) approach to significantly improve the performance of state-of-the-art neural query encoders on the CEQA task. | ['Yangqiu Song', 'Chen Luo', 'Weiqi Wang', 'Xin Liu', 'Jiaxin Bai'] | 2023-05-30 | null | null | null | null | ['complex-query-answering', 'knowledge-graphs'] | ['knowledge-base', 'knowledge-base'] | [-1.03564188e-01 3.04280072e-01 -4.55631673e-01 -5.88738441e-01
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50834c1f-d826-482a-8457-2bae91c6144d | interbert-vision-and-language-interaction-for | 2003.13198 | null | https://arxiv.org/abs/2003.13198v4 | https://arxiv.org/pdf/2003.13198v4.pdf | InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining | Multi-modal pretraining for learning high-level multi-modal representation is a further step towards deep learning and artificial intelligence. In this work, we propose a novel model, namely InterBERT (BERT for Interaction), which is the first model of our series of multimodal pretraining methods M6 (MultiModality-to-MultiModality Multitask Mega-transformer). The model owns strong capability of modeling interaction between the information flows of different modalities. The single-stream interaction module is capable of effectively processing information of multiple modalilties, and the two-stream module on top preserves the independence of each modality to avoid performance downgrade in single-modal tasks. We pretrain the model with three pretraining tasks, including masked segment modeling (MSM), masked region modeling (MRM) and image-text matching (ITM); and finetune the model on a series of vision-and-language downstream tasks. Experimental results demonstrate that InterBERT outperforms a series of strong baselines, including the most recent multi-modal pretraining methods, and the analysis shows that MSM and MRM are effective for pretraining and our method can achieve performances comparable to BERT in single-modal tasks. Besides, we propose a large-scale dataset for multi-modal pretraining in Chinese, and we develop the Chinese InterBERT which is the first Chinese multi-modal pretrained model. We pretrain the Chinese InterBERT on our proposed dataset of 3.1M image-text pairs from the mobile Taobao, the largest Chinese e-commerce platform. We finetune the model for text-based image retrieval, and recently we deployed the model online for topic-based recommendation. | ['Jie Liu', 'An Yang', 'Junyang Lin', 'Hongxia Yang', 'Yichang Zhang', 'Jingren Zhou'] | 2020-03-30 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 1.68164968e-01 -1.87159494e-01 -3.43607187e-01 -3.19762409e-01
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d76412a3-103e-4fce-85dd-8f04e126bdcf | defext-a-semi-supervised-definition | 1606.02514 | null | http://arxiv.org/abs/1606.02514v1 | http://arxiv.org/pdf/1606.02514v1.pdf | DefExt: A Semi Supervised Definition Extraction Tool | We present DefExt, an easy to use semi supervised Definition Extraction Tool.
DefExt is designed to extract from a target corpus those textual fragments
where a term is explicitly mentioned together with its core features, i.e. its
definition. It works on the back of a Conditional Random Fields based
sequential labeling algorithm and a bootstrapping approach. Bootstrapping
enables the model to gradually become more aware of the idiosyncrasies of the
target corpus. In this paper we describe the main components of the toolkit as
well as experimental results stemming from both automatic and manual
evaluation. We release DefExt as open source along with the necessary files to
run it in any Unix machine. We also provide access to training and test data
for immediate use. | ['Luis Espinosa-Anke', 'Roberto Carlini', 'Francesco Ronzano', 'Horacio Saggion'] | 2016-06-08 | null | null | null | null | ['definition-extraction'] | ['natural-language-processing'] | [ 3.96091133e-01 2.51561165e-01 -4.65598911e-01 -4.48817194e-01
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7712f27c-4a1e-4827-97ae-21dc419929a8 | retrieving-signals-in-the-frequency-domain | null | null | https://openreview.net/forum?id=BylB4kBtwB | https://openreview.net/pdf?id=BylB4kBtwB | Retrieving Signals in the Frequency Domain with Deep Complex Extractors | Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems. Building on recent advances, we propose a novel mechanism for extracting signals in the frequency domain. As a case study, we perform audio source separation in the Fourier domain. Our extraction mechanism could be regarded as a local ensembling method that combines a complex-valued convolutional version of Feature-Wise Linear Modulation (FiLM) and a signal averaging operation. We also introduce a new explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram. Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss. When operating in the complex-valued frequency domain, our deep complex-valued network substantially outperforms its real-valued counterparts even with half the depth and a third of the parameters. Our proposed mechanism improves significantly deep complex-valued networks' performance and we demonstrate the usefulness of its regularizing effect. | ['Christopher J Pal', 'Negar Rostamzadeh', 'Jonathan Binas', 'Mirco Ravanelli', 'Ying Zhang', 'Ousmane Dia', 'Olexa Bilaniuk', 'Chiheb Trabelsi'] | 2019-09-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.43214679e-01 4.30316925e-02 2.19917312e-01 -3.65666181e-01
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feea9126-f9bd-4cc2-a76d-7f5ded4016d9 | modeling-human-like-concept-learning-with | 2306.02797 | null | https://arxiv.org/abs/2306.02797v1 | https://arxiv.org/pdf/2306.02797v1.pdf | Modeling Human-like Concept Learning with Bayesian Inference over Natural Language | We model learning of abstract symbolic concepts by performing Bayesian inference over utterances in natural language. For efficient inference, we use a large language model as a proposal distribution. We fit a prior to human data to better model human learners, and evaluate on both generative and logical concepts. | ['Kevin Ellis'] | 2023-06-05 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.14649840e-01 5.02451479e-01 -2.37105951e-01 -9.59574699e-01
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afd5be5b-b647-48d3-aeed-b25aaf105f3a | relational-graph-learning-on-visual-and | 2011.01619 | null | https://arxiv.org/abs/2011.01619v2 | https://arxiv.org/pdf/2011.01619v2.pdf | Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery | Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally invasive surgery, rich information including surgical videos and robotic kinematics can be recorded, which provide complementary knowledge for understanding surgical gestures. However, existing methods either solely adopt uni-modal data or directly concatenate multi-modal representations, which can not sufficiently exploit the informative correlations inherent in visual and kinematics data to boost gesture recognition accuracies. In this regard, we propose a novel online approach of multi-modal relational graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information through interactive message propagation in the latent feature space. In specific, we first extract embeddings from video and kinematics sequences with temporal convolutional networks and LSTM units. Next, we identify multi-relations in these multi-modal embeddings and leverage them through a hierarchical relational graph learning module. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset, outperforming current uni-modal and multi-modal methods on both suturing and knot typing tasks. Furthermore, we validated our method on in-house visual-kinematics datasets collected with da Vinci Research Kit (dVRK) platforms in two centers, with consistent promising performance achieved. | ['Pheng Ann Heng', 'Yueming Jin', 'Jie Ying Wu', 'Yonghao Long', 'Qi Dou', 'Yun-hui Liu', 'Mathias Unberath', 'Bo Lu'] | 2020-11-03 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [-1.59306869e-01 8.09853226e-02 -7.02840865e-01 -9.05168876e-02
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71fcff22-5522-48db-bc58-875293cca5cf | pre-training-intent-aware-encoders-for-zero | 2305.14827 | null | https://arxiv.org/abs/2305.14827v1 | https://arxiv.org/pdf/2305.14827v1.pdf | Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification | Intent classification (IC) plays an important role in task-oriented dialogue systems as it identifies user intents from given utterances. However, models trained on limited annotations for IC often suffer from a lack of generalization to unseen intent classes. We propose a novel pre-training method for text encoders that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks. By applying this pre-training strategy, we also introduce the pre-trained intent-aware encoder (PIE). Specifically, we first train a tagger to identify key phrases within utterances that are crucial for interpreting intents. We then use these extracted phrases to create examples for pre-training a text encoder in a contrastive manner. As a result, our PIE model achieves up to 5.4% and 4.0% higher accuracy than the previous state-of-the-art pre-trained sentence encoder for the N-way zero- and one-shot settings on four IC datasets. | ['Vittorio Castelli', 'Yi Zhang', 'Salvatore Romeo', 'Raphael Shu', 'Nikolaos Pappas', 'Elman Mansimov', 'James Gung', 'Mujeen Sung'] | 2023-05-24 | null | null | null | null | ['intent-classification', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.28951347e-01 3.45824152e-01 -1.64398491e-01 -7.92366147e-01
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620ef3c3-15f1-4ec2-85b2-b22345a70072 | on-arrhythmia-detection-by-deep-learning-and | 1904.00138 | null | http://arxiv.org/abs/1904.00138v4 | http://arxiv.org/pdf/1904.00138v4.pdf | On Arrhythmia Detection by Deep Learning and Multidimensional Representation | An electrocardiogram (ECG) is a time-series signal that is represented by
one-dimensional (1-D) data. Higher dimensional representation contains more
information that is accessible for feature extraction. Hidden variables such as
frequency relation and morphology of segment is not directly accessible in the
time domain. In this paper, 1-D time series data is converted into
multi-dimensional representation in the form of multichannel 2-D images.
Following that, deep learning was used to train a deep neural network based
classifier to detect arrhythmias. The results of simulation on testing database
demonstrate the effectiveness of the proposed methodology by showing an
outstanding classification performance compared to other existing methods and
hand-crafted annotations made by certified cardiologists. | ['S. Wibowo', 'C. Hao', 'M. Majmudar', 'K. S. Rajput'] | 2019-03-30 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 3.18363190e-01 -2.46201959e-02 1.32254988e-01 -4.03489739e-01
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f4141c4a-f5c9-43d6-8bdb-f53124f8e1d5 | challenges-and-considerations-with-code-mixed | 2106.07823 | null | https://arxiv.org/abs/2106.07823v1 | https://arxiv.org/pdf/2106.07823v1.pdf | Challenges and Considerations with Code-Mixed NLP for Multilingual Societies | Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech. This paper discusses the current state of the NLP research, limitations, and foreseeable pitfalls in addressing five real-world applications for social good crisis management, healthcare, political campaigning, fake news, and hate speech for multilingual societies. We also propose futuristic datasets, models, and tools that can significantly advance the current research in multilingual NLP applications for the societal good. As a representative example, we consider English-Hindi code-mixing but draw similar inferences for other language pairs | ['Mayank Singh', 'Vivek Srivastava'] | 2021-06-15 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-3.17113817e-01 3.49605381e-01 -3.64089549e-01 -6.62273727e-04
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728b44d3-46d8-4fe8-96e1-18d985dd8a8e | learning-to-perceive-in-deep-model-free | 2301.03730 | null | https://arxiv.org/abs/2301.03730v2 | https://arxiv.org/pdf/2301.03730v2.pdf | Learning to Perceive in Deep Model-Free Reinforcement Learning | This work proposes a novel model-free Reinforcement Learning (RL) agent that is able to learn how to complete an unknown task having access to only a part of the input observation. We take inspiration from the concepts of visual attention and active perception that are characteristic of humans and tried to apply them to our agent, creating a hard attention mechanism. In this mechanism, the model decides first which region of the input image it should look at, and only after that it has access to the pixels of that region. Current RL agents do not follow this principle and we have not seen these mechanisms applied to the same purpose as this work. In our architecture, we adapt an existing model called recurrent attention model (RAM) and combine it with the proximal policy optimization (PPO) algorithm. We investigate whether a model with these characteristics is capable of achieving similar performance to state-of-the-art model-free RL agents that access the full input observation. This analysis is made in two Atari games, Pong and SpaceInvaders, which have a discrete action space, and in CarRacing, which has a continuous action space. Besides assessing its performance, we also analyze the movement of the attention of our model and compare it with what would be an example of the human behavior. Even with such visual limitation, we show that our model matches the performance of PPO+LSTM in two of the three games tested. | ['Francisco S. Melo', 'Alberto Sardinha', 'Gonçalo Querido'] | 2023-01-10 | null | null | null | null | ['hard-attention', 'atari-games'] | ['methodology', 'playing-games'] | [ 1.23021556e-02 4.64236826e-01 9.59751457e-02 2.22600043e-01
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ba7e5649-2a01-4561-9539-f8d025529c5d | structure-aware-generation-network-for-recipe | 2009.00944 | null | https://arxiv.org/abs/2009.00944v1 | https://arxiv.org/pdf/2009.00944v1.pdf | Structure-Aware Generation Network for Recipe Generation from Images | Sharing food has become very popular with the development of social media. For many real-world applications, people are keen to know the underlying recipes of a food item. In this paper, we are interested in automatically generating cooking instructions for food. We investigate an open research task of generating cooking instructions based on only food images and ingredients, which is similar to the image captioning task. However, compared with image captioning datasets, the target recipes are long-length paragraphs and do not have annotations on structure information. To address the above limitations, we propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the inferred tree structures with the recipe generation procedure. Our proposed model can produce high-quality and coherent recipes, and achieve the state-of-the-art performance on the benchmark Recipe1M dataset. | ['Steven C. H. Hoi', 'Chunyan Miao', 'Hao Wang', 'Guosheng Lin'] | 2020-09-02 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5757_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720358.pdf | eccv-2020-8 | ['recipe-generation'] | ['miscellaneous'] | [ 6.55268788e-01 2.29520723e-01 -5.51504642e-02 -4.48306799e-01
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b3ae3550-5905-4ba7-83ac-acd9d5fc9829 | comparison-of-binaural-rtf-vector-based | 2104.05079 | null | https://arxiv.org/abs/2104.05079v2 | https://arxiv.org/pdf/2104.05079v2.pdf | Comparison of Binaural RTF-Vector-Based Direction of Arrival Estimation Methods Exploiting an External Microphone | In this paper we consider a binaural hearing aid setup, where in addition to the head-mounted microphones an external microphone is available. For this setup, we investigate the performance of several relative transfer function (RTF) vector estimation methods to estimate the direction of arrival (DOA) of the target speaker in a noisy and reverberant acoustic environment. More in particular, we consider the state-of-the-art covariance whitening (CW) and covariance subtraction (CS) methods, either incorporating the external microphone or not, and the recently proposed spatial coherence (SC) method, requiring the external microphone. To estimate the DOA from the estimated RTF vector, we propose to minimize the frequency-averaged Hermitian angle between the estimated head-mounted RTF vector and a database of prototype head-mounted RTF vectors. Experimental results with stationary and moving speech sources in a reverberant environment with diffuse-like noise show that the SC method outperforms the CS method and yields a similar DOA estimation accuracy as the CW method at a lower computational complexity. | ['Simon Doclo', 'Daniel Fejgin'] | 2021-04-11 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.01294407e-01 -3.98540914e-01 1.09714890e+00 4.60492680e-03
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0590429a-c70a-4809-91cd-ade1c328834d | lightning-fast-video-anomaly-detection-via | 2211.15597 | null | https://arxiv.org/abs/2211.15597v1 | https://arxiv.org/pdf/2211.15597v1.pdf | Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation | We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and adversarial distillation, introducing an adversarial discriminator for each teacher to distinguish between target and generated anomaly maps. We conduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2), showing that our method is over 7 times faster than the fastest competing method, and between 28 and 62 times faster than object-centric models, while obtaining comparable results to recent methods. Our evaluation also indicates that our model achieves the best trade-off between speed and accuracy, due to its previously unheard-of speed of 1480 FPS. In addition, we carry out a comprehensive ablation study to justify our architectural design choices. | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Radu Tudor Ionescu', 'Dana Dascalescu', 'Florinel-Alin Croitoru', 'Nicolae-Catalin Ristea'] | 2022-11-28 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-3.06116082e-02 -3.80913764e-02 1.60036474e-01 -1.62107795e-01
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84243748-d6fe-44d2-9177-1bc4b3ff3575 | object-pose-estimation-with-statistical | 2303.12246 | null | https://arxiv.org/abs/2303.12246v1 | https://arxiv.org/pdf/2303.12246v1.pdf | Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation | The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namely conformal keypoint detection and geometric uncertainty propagation, into the two-stage paradigm and propose the first pose estimator that endows an estimation with provable and computable worst-case error bounds. On one hand, conformal keypoint detection applies the statistical machinery of inductive conformal prediction to convert heuristic keypoint detections into circular or elliptical prediction sets that cover the groundtruth keypoints with a user-specified marginal probability (e.g., 90%). Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability. The PURSE, however, is a nonconvex set that does not directly lead to estimated poses and uncertainties. Therefore, we develop RANdom SAmple averaGing (RANSAG) to compute an average pose and apply semidefinite relaxation to upper bound the worst-case errors between the average pose and the groundtruth. On the LineMOD Occlusion dataset we demonstrate: (i) the PURSE covers the groundtruth with valid probabilities; (ii) the worst-case error bounds provide correct uncertainty quantification; and (iii) the average pose achieves better or similar accuracy as representative methods based on sparse keypoints. | ['Marco Pavone', 'Heng Yang'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['keypoint-detection'] | ['computer-vision'] | [-6.28095195e-02 5.52398145e-01 -1.55838445e-01 -4.68887165e-02
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a515f1c7-752e-4cf3-ad2f-1725a1a333ee | progressive-attention-memory-network-for | 1904.08607 | null | http://arxiv.org/abs/1904.08607v1 | http://arxiv.org/pdf/1904.08607v1.pdf | Progressive Attention Memory Network for Movie Story Question Answering | This paper proposes the progressive attention memory network (PAMN) for movie
story question answering (QA). Movie story QA is challenging compared to VQA in
two aspects: (1) pinpointing the temporal parts relevant to answer the question
is difficult as the movies are typically longer than an hour, (2) it has both
video and subtitle where different questions require different modality to
infer the answer. To overcome these challenges, PAMN involves three main
features: (1) progressive attention mechanism that utilizes cues from both
question and answer to progressively prune out irrelevant temporal parts in
memory, (2) dynamic modality fusion that adaptively determines the contribution
of each modality for answering the current question, and (3) belief correction
answering scheme that successively corrects the prediction score on each
candidate answer. Experiments on publicly available benchmark datasets, MovieQA
and TVQA, demonstrate that each feature contributes to our movie story QA
architecture, PAMN, and improves performance to achieve the state-of-the-art
result. Qualitative analysis by visualizing the inference mechanism of PAMN is
also provided. | ['Kyung-Su Kim', 'Sungjin Kim', 'Junyeong Kim', 'Minuk Ma', 'Chang D. Yoo'] | 2019-04-18 | progressive-attention-memory-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Progressive_Attention_Memory_Network_for_Movie_Story_Question_Answering_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Progressive_Attention_Memory_Network_for_Movie_Story_Question_Answering_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-story-qa'] | ['computer-vision'] | [ 2.27082804e-01 -6.89617470e-02 -1.07211478e-01 -2.72177786e-01
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4bc8b7c5-958b-466a-a733-50820f02dd3e | maximum-entropy-regularized-multi-goal | 1905.08786 | null | https://arxiv.org/abs/1905.08786v3 | https://arxiv.org/pdf/1905.08786v3.pdf | Maximum Entropy-Regularized Multi-Goal Reinforcement Learning | In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biased towards the behavior policies. From a Bayesian perspective, when there is no prior knowledge about the target goal distribution, the agent should learn uniformly from diverse achieved goals. Therefore, we first propose a novel multi-goal RL objective based on weighted entropy. This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals. Secondly, we developed a maximum entropy-based prioritization framework to optimize the proposed objective. For evaluation of this framework, we combine it with Deep Deterministic Policy Gradient, both with or without Hindsight Experience Replay. On a set of multi-goal robotic tasks of OpenAI Gym, we compare our method with other baselines and show promising improvements in both performance and sample-efficiency. | ['Volker Tresp', 'Rui Zhao', 'Xudong Sun'] | 2019-05-21 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.90975770e-01 4.08059448e-01 -2.10363969e-01 -2.77250916e-01
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8f2268e9-90df-43e3-b4cf-bf355a523ba3 | plgan-generative-adversarial-networks-for | 2204.07243 | null | https://arxiv.org/abs/2204.07243v1 | https://arxiv.org/pdf/2204.07243v1.pdf | PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images | Accurate segmentation of power lines in various aerial images is very important for UAV flight safety. The complex background and very thin structures of power lines, however, make it an inherently difficult task in computer vision. This paper presents PLGAN, a simple yet effective method based on generative adversarial networks, to segment power lines from aerial images with different backgrounds. Instead of directly using the adversarial networks to generate the segmentation, we take their certain decoding features and embed them into another semantic segmentation network by considering more context, geometry, and appearance information of power lines. We further exploit the appropriate form of the generated images for high-quality feature embedding and define a new loss function in the Hough-transform parameter space to enhance the segmentation of very thin power lines. Extensive experiments and comprehensive analysis demonstrate that our proposed PLGAN outperforms the prior state-of-the-art methods for semantic segmentation and line detection. | ['Song Wang', 'XiaoFeng Wang', 'Rabab Abdelfattah'] | 2022-04-14 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.63409752e-01 -1.61317140e-01 2.20538482e-01 -3.67962271e-02
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5532c112-6270-4c1a-abb8-afcb0dcbb0dd | cuni-submission-to-mt4all-shared-task | null | null | https://aclanthology.org/2022.sigul-1.10 | https://aclanthology.org/2022.sigul-1.10.pdf | CUNI Submission to MT4All Shared Task | This paper describes our submission to the MT4All Shared Task in unsupervised machine translation from English to Ukrainian, Kazakh and Georgian in the legal domain. In addition to the standard pipeline for unsupervised training (pretraining followed by denoising and back-translation), we used supervised training on a pseudo-parallel corpus retrieved from the provided mono-lingual corpora. Our system scored significantly higher than the baseline hybrid unsupervised MT system. | ['Ondrej Bojar', 'Ivana Kvapilíková'] | null | null | null | null | sigul-lrec-2022-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.38812590e-01 3.29725534e-01 -3.78703266e-01 -6.24473691e-01
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73ceab22-a7db-46a4-b482-e3076055d6f6 | oakink-a-large-scale-knowledge-repository-for | 2203.15709 | null | https://arxiv.org/abs/2203.15709v1 | https://arxiv.org/pdf/2203.15709v1.pdf | OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction | Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive awareness of them. In this work, we propose a multi-modal and rich-annotated knowledge repository, OakInk, for visual and cognitive understanding of hand-object interactions. We start to collect 1,800 common household objects and annotate their affordances to construct the first knowledge base: Oak. Given the affordance, we record rich human interactions with 100 selected objects in Oak. Finally, we transfer the interactions on the 100 recorded objects to their virtual counterparts through a novel method: Tink. The recorded and transferred hand-object interactions constitute the second knowledge base: Ink. As a result, OakInk contains 50,000 distinct affordance-aware and intent-oriented hand-object interactions. We benchmark OakInk on pose estimation and grasp generation tasks. Moreover, we propose two practical applications of OakInk: intent-based interaction generation and handover generation. Our datasets and source code are publicly available at https://github.com/lixiny/OakInk. | ['Cewu Lu', 'Liu Liu', 'Anran Xu', 'Fei Wu', 'Xinyu Zhan', 'Kailin Li', 'Lixin Yang'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['grasp-generation'] | ['computer-vision'] | [-8.73998478e-02 -1.65346369e-01 -2.05885723e-01 -3.15659285e-01
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2d0e5ea9-bfdb-4ed7-a670-51bc4429badf | 2305-14550 | 2305.14550 | null | https://arxiv.org/abs/2305.14550v2 | https://arxiv.org/pdf/2305.14550v2.pdf | Sequence Modeling is a Robust Contender for Offline Reinforcement Learning | Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset. Three major paradigms for offline RL are Q-Learning, Imitation Learning, and Sequence Modeling. A key open question is: which paradigm is preferred under what conditions? We study this question empirically by exploring the performance of representative algorithms -- Conservative Q-Learning (CQL), Behavior Cloning (BC), and Decision Transformer (DT) -- across the commonly used D4RL and Robomimic benchmarks. We design targeted experiments to understand their behavior concerning data suboptimality and task complexity. Our key findings are: (1) Sequence Modeling requires more data than Q-Learning to learn competitive policies but is more robust; (2) Sequence Modeling is a substantially better choice than both Q-Learning and Imitation Learning in sparse-reward and low-quality data settings; and (3) Sequence Modeling and Imitation Learning are preferable as task horizon increases, or when data is obtained from human demonstrators. Based on the overall strength of Sequence Modeling, we also investigate architectural choices and scaling trends for DT on Atari and D4RL and make design recommendations. We find that scaling the amount of data for DT by 5x gives a 2.5x average score improvement on Atari. | ['Amy Zhang', 'Shagun Sodhani', 'Alborz Geramifard', 'Rohan Chitnis', 'Prajjwal Bhargava'] | 2023-05-23 | null | null | null | null | ['q-learning', 'open-question', 'offline-rl', 'd4rl'] | ['methodology', 'natural-language-processing', 'playing-games', 'robots'] | [-2.23346099e-01 -2.18770832e-01 -6.54088438e-01 -1.01821057e-01
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764170a9-753d-433b-8f81-7e101cd87d9e | image-classification-using-sequence-of-pixels | 2209.11495 | null | https://arxiv.org/abs/2209.11495v1 | https://arxiv.org/pdf/2209.11495v1.pdf | Image Classification using Sequence of Pixels | This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc. We also review the state-of-the-art sequential image classification architectures. We mainly focus on LSTM, BiLSTM, temporal convolution network, and independent recurrent neural network architecture in the study. It is known that RNN lacks in learning long-term dependencies in the input sequence. We use a simple feature construction method using orthogonal Ramanujan periodic transform on the input sequence. Experiments demonstrate that if these features are given to LSTM or BiLSTM networks, the performance increases drastically. Our focus in this study is to increase the training accuracy simultaneously reducing the training time for the LSTM and BiLSTM architecture, but not on pushing the state-of-the-art results, so we use simple LSTM/BiLSTM architecture. We compare sequential input with the constructed feature as input to single layer LSTM and BiLSTM network for MNIST and CIFAR datasets. We observe that sequential input to the LSTM network with 128 hidden unit training for five epochs results in training accuracy of 33% whereas constructed features as input to the same LSTM network results in training accuracy of 90% with 1/3 lesser time. | ['Gajraj Kuldeep'] | 2022-09-23 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [ 5.03906190e-01 -1.62608296e-01 -6.83963299e-02 -2.95945317e-01
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44efb963-3ca9-4399-9174-cac3c99b59fd | situated-incremental-natural-language | null | null | https://aclanthology.org/C14-1170 | https://aclanthology.org/C14-1170.pdf | Situated Incremental Natural Language Understanding using a Multimodal, Linguistically-driven Update Model | null | ['Spyros Kousidis', 'David Schlangen', 'Casey Kennington'] | 2014-08-01 | situated-incremental-natural-language-1 | https://aclanthology.org/C14-1170 | https://aclanthology.org/C14-1170.pdf | coling-2014-8 | ['dialogue-management'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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f5a9e98e-b33b-4cbb-be3c-a5c8736aef18 | morphological-development-at-the-evolutionary | 2010.14894 | null | https://arxiv.org/abs/2010.14894v2 | https://arxiv.org/pdf/2010.14894v2.pdf | Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution | Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy -- evolution overarching development -- is observed for most multicellular lifeforms. When designing robots however, this tenet lifts: it becomes -- however natural -- a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots' morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics. | ['Jun Tani', 'Fabien C. Y. Benureau'] | 2020-10-28 | null | null | null | null | ['developmental-learning'] | ['robots'] | [-2.33700544e-01 3.41230899e-01 1.43859208e-01 4.01284605e-01
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6b241911-a054-470f-8e07-12bb0935fb43 | learning-to-find-proofs-and-theorems-by | 2205.14229 | null | https://arxiv.org/abs/2205.14229v3 | https://arxiv.org/pdf/2205.14229v3.pdf | Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis | We propose a new approach to automated theorem proving where an AlphaZero-style agent is self-training to refine a generic high-level expert strategy expressed as a nondeterministic program. An analogous teacher agent is self-training to generate tasks of suitable relevance and difficulty for the learner. This allows leveraging minimal amounts of domain knowledge to tackle problems for which training data is unavailable or hard to synthesize. As a specific illustration, we consider loop invariant synthesis for imperative programs and use neural networks to refine both the teacher and solver strategies. | ['André Platzer', 'Jonathan Laurent'] | 2022-05-27 | null | null | null | null | ['program-synthesis', 'automated-theorem-proving', 'automated-theorem-proving'] | ['computer-code', 'miscellaneous', 'reasoning'] | [ 4.72710937e-01 1.09578073e+00 -2.95757085e-01 -2.28240222e-01
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f61c5901-683f-4514-91bc-1c2e418e5f2a | weakly-supervised-3d-classification-of-chest | 2011.00149 | null | https://arxiv.org/abs/2011.00149v1 | https://arxiv.org/pdf/2011.00149v1.pdf | Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features | Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning segmentation and classification models extract distinctly unique combinations of anatomical features from the same target class(es), they are typically seen as two independent processes in a computer-aided diagnosis (CAD) pipeline, with little to no feature reuse. In this research, we propose a medical classifier that leverages the semantic structural concepts learned via multi-resolution segmentation feature maps, to guide weakly supervised 3D classification of chest CT volumes. Additionally, a comparative analysis is drawn across two different types of feature aggregation to explore the vast possibilities surrounding feature fusion. Using a dataset of 1593 scans labeled on a case-level basis via rule-based model, we train a dual-stage convolutional neural network (CNN) to perform organ segmentation and binary classification of four representative diseases (emphysema, pneumonia/atelectasis, mass and nodules) in lungs. The baseline model, with separate stages for segmentation and classification, results in AUC of 0.791. Using identical hyperparameters, the connected architecture using static and dynamic feature aggregation improves performance to AUC of 0.832 and 0.851, respectively. This study advances the field in two key ways. First, case-level report data is used to weakly supervise a 3D CT classifier of multiple, simultaneous diseases for an organ. Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance. | ['Joseph Y. Lo', 'Geoffrey D. Rubin', 'Maciej A. Mazurowski', 'Rui Hou', "Vincent M. D'Anniballe", 'Khrystyna Faryna', 'Fakrul I. Tushar', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 4.78730023e-01 4.66011256e-01 -4.21277463e-01 -5.48997164e-01
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cbd33f01-f5ae-46f5-b179-5a59604db4f7 | discontinuous-constituency-and-bert-a-case-1 | 2203.01063 | null | https://arxiv.org/abs/2203.01063v2 | https://arxiv.org/pdf/2203.01063v2.pdf | Discontinuous Constituency and BERT: A Case Study of Dutch | In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch. We devise a test suite based on a mildly context-sensitive formalism, from which we derive grammars that capture the linguistic phenomena of control verb nesting and verb raising. The grammars, paired with a small lexicon, provide us with a large collection of naturalistic utterances, annotated with verb-subject pairings, that serve as the evaluation test bed for an attention-based span selection probe. Our results, backed by extensive analysis, suggest that the models investigated fail in the implicit acquisition of the dependencies examined. | ['Gijs Wijnholds', 'Konstantinos Kogkalidis'] | 2022-03-02 | null | https://aclanthology.org/2022.findings-acl.298 | https://aclanthology.org/2022.findings-acl.298.pdf | findings-acl-2022-5 | ['probing-language-models'] | ['natural-language-processing'] | [ 1.50905266e-01 2.81380147e-01 1.52466998e-01 -5.00007510e-01
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0dbce5f7-781d-4314-b298-c420e939c4a2 | association-of-genomic-subtypes-of-lower | 1906.03720 | null | https://arxiv.org/abs/1906.03720v1 | https://arxiv.org/pdf/1906.03720v1.pdf | Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm | Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. To analyze the relationship between the imaging features and genomic clusters, we conducted the Fisher exact test for 10 hypotheses for each pair of imaging feature and genomic subtype. To account for multiple hypothesis testing, we applied a Bonferroni correction. P-values lower than 0.005 were considered statistically significant. We found the strongest association between RNASeq clusters and the bounding ellipsoid volume ratio ($p<0.0002$) and between RNASeq clusters and margin fluctuation ($p<0.005$). In addition, we identified associations between bounding ellipsoid volume ratio and all tested molecular subtypes ($p<0.02$) as well as between angular standard deviation and RNASeq cluster ($p<0.02$). In terms of automatic tumor segmentation that was used to generate the quantitative image characteristics, our deep learning algorithm achieved a mean Dice coefficient of 82% which is comparable to human performance. | ['Maciej A. Mazurowski', 'Mateusz Buda', 'Ashirbani Saha'] | 2019-06-09 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-5.08019552e-02 3.37766737e-01 -1.03530072e-01 -2.34595954e-01
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c49c1e48-7204-41f0-89fd-5bf3c01e6f63 | swincross-cross-modal-swin-transformer-for | 2302.03861 | null | https://arxiv.org/abs/2302.03861v1 | https://arxiv.org/pdf/2302.03861v1.pdf | SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images | Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long-range dependency is still limited, and this can result in sub-optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long-range information in several semantic segmentation tasks performed on medical images. Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions.To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR. The proposed method is experimentally shown to outperform these comparing methods thanks to the ability of the CMA module to capture better inter-modality complimentary feature representations between PET and CT, for the task of head-and-neck tumor segmentation. | ['Quanzheng Li', 'Kuang Gong', 'Se-In Jang', 'Junyu Chen', 'Gary Y. Li'] | 2023-02-08 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.14693516e-01 6.01489209e-02 -5.12778223e-01 -2.21287653e-01
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95e2b55d-af5e-448e-af9b-1b2ded506bdb | evaluating-the-stability-of-semantic-concept | 2304.14864 | null | https://arxiv.org/abs/2304.14864v1 | https://arxiv.org/pdf/2304.14864v1.pdf | Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability | Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving. However, to use the concept representations for safety-relevant purposes, like inspection or error retrieval, these must be of high quality and, in particular, stable. This paper focuses on two stability goals when working with concept representations in computer vision CNNs: stability of concept retrieval and of concept attribution. The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted. To address concept retrieval stability, we propose a novel metric that considers both concept separation and consistency, and is agnostic to layer and concept representation dimensionality. We then investigate impacts of concept abstraction level, number of concept training samples, CNN size, and concept representation dimensionality on stability. For concept attribution stability we explore the effect of gradient instability on gradient-based explainability methods. The results on various CNNs for classification and object detection yield the main findings that (1) the stability of concept retrieval can be enhanced through dimensionality reduction via data aggregation, and (2) in shallow layers where gradient instability is more pronounced, gradient smoothing techniques are advised. Finally, our approach provides valuable insights into selecting the appropriate layer and concept representation dimensionality, paving the way towards CA in safety-critical XAI applications. | ['Korinna Bade', 'Christian Hellert', 'Gesina Schwalbe', 'Georgii Mikriukov'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.58227581e-01 3.45042318e-01 -5.33180647e-02 -4.94576871e-01
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c6c8c4d4-b297-4a25-bb1e-28f5dbb65128 | one-class-classifiers-based-on-entropic | 1604.02477 | null | http://arxiv.org/abs/1604.02477v4 | http://arxiv.org/pdf/1604.02477v4.pdf | One-class classifiers based on entropic spanning graphs | One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The spanning graph is learned on the embedded input data and the
outcoming partition of vertices defines the classifier. The final partition is
derived by exploiting a criterion based on mutual information minimization.
Here, we compute the mutual information by using a convenient formulation
provided in terms of the $\alpha$-Jensen difference. Once training is
completed, in order to associate a confidence level with the classifier
decision, a graph-based fuzzy model is constructed. The fuzzification process
is based only on topological information of the vertices of the entropic
spanning graph. As such, the proposed one-class classifier is suitable also for
data characterized by complex geometric structures. We provide experiments on
well-known benchmarks containing both feature vectors and labeled graphs. In
addition, we apply the method to the protein solubility recognition problem by
considering several representations for the input samples. Experimental results
demonstrate the effectiveness and versatility of the proposed method with
respect to other state-of-the-art approaches. | ['Cesare Alippi', 'Lorenzo Livi'] | 2016-04-08 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.16885224e-01 2.74260193e-01 8.36086199e-02 -5.19732833e-01
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b1ab4ac6-51a1-4811-b3e8-faee5eadee4a | connotation-in-translation | null | null | https://aclanthology.org/W15-2903 | https://aclanthology.org/W15-2903.pdf | Connotation in Translation | null | ['Marine Carpuat'] | 2015-09-01 | null | null | null | ws-2015-9 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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356a35d2-658f-4f1f-866e-94c4049a20b5 | k-means-clustering-and-ensemble-of | 1702.07333 | null | http://arxiv.org/abs/1702.07333v1 | http://arxiv.org/pdf/1702.07333v1.pdf | k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge | This abstract briefly describes a segmentation algorithm developed for the
ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of
the competition is to perform a segmentation (in the form of a binary mask
image) of skin lesions in dermoscopic images as close as possible to a
segmentation performed by trained clinicians, which is taken as ground truth.
This project only takes part in the segmentation phase of the challenge. The
other phases of the competition (feature extraction and lesion identification)
are not considered.
The proposed algorithm consists of 4 steps: (1) lesion image preprocessing,
(2) image segmentation using k-means clustering of pixel colors, (3)
calculation of a set of features describing the properties of each segmented
region, and (4) calculation of a final score for each region, representing the
likelihood of corresponding to a suitable lesion segmentation. The scores in
step (4) are obtained by averaging the results of 2 different regression models
using the scores of each region as input. Before using the algorithm these
regression models must be trained using the training set of images and ground
truth masks provided by the Competition. Steps 2 to 4 are repeated with an
increasing number of clusters (and therefore the image is segmented into more
regions) until there is no further improvement of the calculated scores. | ['Monica Iglesias', 'David Alvarez'] | 2017-02-23 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.20591688e-01 1.15110435e-01 -7.72752762e-02 -3.19997966e-01
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7ef6231e-c8ca-408e-af76-278153bd1263 | an-efficient-and-layout-independent-automatic | 1909.01754 | null | https://arxiv.org/abs/1909.01754v4 | https://arxiv.org/pdf/1909.01754v4.pdf | An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector | This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community. | ['David Menotti', 'Gabriel R. Gonçalves', 'Rayson Laroca', 'William Robson Schwartz', 'Luiz A. Zanlorensi', 'Eduardo Todt'] | 2019-09-04 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.51997030e-01 -7.66757846e-01 5.56362830e-02 -2.70064652e-01
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3de76c6f-4bdd-42e9-8caf-53e17005e72b | learn-how-to-prune-pixels-for-multi-view | 2305.03572 | null | https://arxiv.org/abs/2305.03572v1 | https://arxiv.org/pdf/2305.03572v1.pdf | Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis | Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown good performance in terms of objective and subjective quality, the research community devotes great effort to their improvement. However, the large volume of data necessary to render at the receiver's side hinders applications in limited bandwidth environments or prevents their employment in real-time applications. We present LeHoPP, a method for input pixel pruning, where we examine the importance of each input pixel concerning the rendered view, and we avoid the use of irrelevant pixels. Even without retraining the image-based rendering network, our approach shows a good trade-off between synthesis quality and pixel rate. When tested in the general neural rendering framework, compared to other pruning baselines, LeHoPP gains between $0.9$ dB and $3.6$ dB on average. | ['Félix Henry', 'Marco Cagnazzo', 'Enzo Tartaglione', 'Marta Milovanović'] | 2023-05-05 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 6.74266875e-01 1.07475422e-01 2.17605308e-01 -3.03941131e-01
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ec9b86d8-1c2f-49f7-9184-bd03af8d60d7 | animating-face-using-disentangled-audio | 1910.00726 | null | https://arxiv.org/abs/1910.00726v1 | https://arxiv.org/pdf/1910.00726v1.pdf | Animating Face using Disentangled Audio Representations | All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone. As we show empirically, one can easily break these systems by simply adding certain background noise to the utterance or changing its emotional tone (to such as sad). To make talking head generation robust to such variations, we propose an explicit audio representation learning framework that disentangles audio sequences into various factors such as phonetic content, emotional tone, background noise and others. We conduct experiments to validate that conditioned on disentangled content representation, the generated mouth movement by our model is significantly more accurate than previous approaches (without disentangled learning) in the presence of noise and emotional variations. We further demonstrate that our framework is compatible with current state-of-the-art approaches by replacing their original audio learning component with ours. To our best knowledge, this is the first work which improves the performance of talking head generation from disentangled audio representation perspective, which is important for many real-world applications. | ['Gaurav Mittal', 'Baoyuan Wang'] | 2019-10-02 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 3.67614567e-01 2.85975724e-01 -1.64994210e-01 -3.68204325e-01
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fbbc9840-094c-4a59-9458-edb0e3db760a | compositional-generalization-in-grounded | 2207.02518 | null | https://arxiv.org/abs/2207.02518v1 | https://arxiv.org/pdf/2207.02518v1.pdf | Compositional Generalization in Grounded Language Learning via Induced Model Sparsity | We provide a study of how induced model sparsity can help achieve compositional generalization and better sample efficiency in grounded language learning problems. We consider simple language-conditioned navigation problems in a grid world environment with disentangled observations. We show that standard neural architectures do not always yield compositional generalization. To address this, we design an agent that contains a goal identification module that encourages sparse correlations between words in the instruction and attributes of objects, composing them together to find the goal. The output of the goal identification module is the input to a value iteration network planner. Our agent maintains a high level of performance on goals containing novel combinations of properties even when learning from a handful of demonstrations. We examine the internal representations of our agent and find the correct correspondences between words in its dictionary and attributes in the environment. | ['Alexander Ilin', 'Sam Spilsbury'] | 2022-07-06 | null | https://aclanthology.org/2022.naacl-srw.19 | https://aclanthology.org/2022.naacl-srw.19.pdf | naacl-acl-2022-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.65814713e-01 3.14245522e-01 -1.57724947e-01 -1.45636082e-01
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c719d964-bbd4-4492-80ec-3e9190a51bae | underwater-object-detection-using-invert | 2005.11552 | null | https://arxiv.org/abs/2005.11552v1 | https://arxiv.org/pdf/2005.11552v1.pdf | Underwater object detection using Invert Multi-Class Adaboost with deep learning | In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images in the underwater datasets and real applications accompany heterogeneous noise. To address these two problems, we first propose a novel neural network architecture, namely Sample-WeIghted hyPEr Network (SWIPENet), for small object detection. SWIPENet consists of high resolution and semantic rich Hyper Feature Maps which can significantly improve small object detection accuracy. In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet. Experiments on two underwater robot picking contest datasets URPC2017 and URPC2018 show that the proposed SWIPENet+IMA framework achieves better performance in detection accuracy against several state-of-the-art object detection approaches. | ['Huiyu Zhou', 'ShengKe Wang', 'Lei Tong', 'Long Chen', 'Junyu Dong', 'Zheheng Jiang', 'Zhihua Liu'] | 2020-05-23 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 7.06709996e-02 -3.75228822e-01 6.56622946e-01 -3.64990145e-01
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753810fa-7a35-4a54-81b5-12c2ffd959af | 30m-resolution-global-annual-burned-area | 1805.02579 | null | http://arxiv.org/abs/1805.02579v1 | http://arxiv.org/pdf/1805.02579v1.pdf | 30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine | Heretofore, global burned area (BA) products are only available at coarse
spatial resolution, since most of the current global BA products are produced
with the help of active fire detection or dense time-series change analysis,
which requires very high temporal resolution. In this study, however, we focus
on automated global burned area mapping approach based on Landsat images. By
utilizing the huge catalog of satellite imagery as well as the high-performance
computing capacity of Google Earth Engine, we proposed an automated pipeline
for generating 30-meter resolution global-scale annual burned area map from
time-series of Landsat images, and a novel 30-meter resolution global annual
burned area map of 2015 (GABAM 2015) is released. GABAM 2015 consists of
spatial extent of fires that occurred during 2015 and not of fires that
occurred in previous years. Cross-comparison with recent Fire_cci version 5.0
BA product found a similar spatial distribution and a strong correlation
($R^2=0.74$) between the burned areas from the two products, although
differences were found in specific land cover categories (particularly in
agriculture land). Preliminary global validation showed the commission and
omission error of GABAM 2015 are 13.17% and 30.13%, respectively. | ['Xiaomei Zhang', 'Weili Jiao', 'Zhaoming Zhang', 'Tengfei Long', 'Bingfang Wu', 'Guizhou Wang', 'Ranyu Yin', 'Guojin He', 'Chao Tang'] | 2018-05-07 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.36455372e-01 -4.22292769e-01 -1.13174438e-01 1.13988109e-01
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a4046e4d-c074-4bb8-ab26-9333c8f965c9 | countingmot-joint-counting-detection-and-re | 2212.05861 | null | https://arxiv.org/abs/2212.05861v2 | https://arxiv.org/pdf/2212.05861v2.pdf | CountingMOT: Joint Counting, Detection and Re-Identification for Multiple Object Tracking | The recent trend in multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 79.7), MOT17 (MOTA of 81.3%) and MOT20 (MOTA of 78.9%). | ['Yuhang Shi', 'Bowen Chen', 'Hui Cao', 'Denglu Wu', 'Honghai Liu', 'Weibo Jiang', 'Weihong Ren'] | 2022-12-12 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-9.74039212e-02 -5.16707659e-01 1.62398905e-01 3.43894437e-02
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f5d45739-1bc8-4233-85fa-8b7cac233e26 | weakly-supervised-few-shot-object | 2001.09540 | null | https://arxiv.org/abs/2001.09540v3 | https://arxiv.org/pdf/2001.09540v3.pdf | Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings | Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-level, scribbles and bounding box supervision. This paper takes another approach, i.e., only requiring image-level label for few-shot object segmentation. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding. Our model using image-level labels achieves 4.8% improvement over previously proposed image-level few-shot object segmentation. It also outperforms state-of-the-art methods that use weak bounding box supervision on PASCAL-5i. Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels. We further propose a novel setup, Temporal Object Segmentation for Few-shot Learning (TOSFL) for videos. TOSFL can be used on a variety of public video data such as Youtube-VOS, as demonstrated in both instance-level and category-level TOSFL experiments. | ['Boris N. Oreshkin', 'Martin Jagersand', 'Mennatullah Siam', 'Hengshuai Yao', 'Naren Doraiswamy'] | 2020-01-26 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.86025113e-01 -6.12292811e-03 -5.35102963e-01 -6.11696720e-01
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159b66db-9c6c-498a-892a-d9da76bf2429 | scarp-3d-shape-completion-in-arbitrary-poses | 2301.07213 | null | https://arxiv.org/abs/2301.07213v1 | https://arxiv.org/pdf/2301.07213v1.pdf | SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping | Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods tackle this problem by training 3D shape generation networks to learn a prior over the full 3D shapes. In this training regime, the methods expect the inputs to be in a fixed canonical form, without which they fail to learn a valid prior over the 3D shapes. We propose SCARP, a model that performs Shape Completion in ARbitrary Poses. Given a partial pointcloud of an object, SCARP learns a disentangled feature representation of pose and shape by relying on rotationally equivariant pose features and geometric shape features trained using a multi-tasking objective. Unlike existing methods that depend on an external canonicalization, SCARP performs canonicalization, pose estimation, and shape completion in a single network, improving the performance by 45% over the existing baselines. In this work, we use SCARP for improving grasp proposals on tabletop objects. By completing partial tabletop objects directly in their observed poses, SCARP enables a SOTA grasp proposal network improve their proposals by 71.2% on partial shapes. Project page: https://bipashasen.github.io/scarp | ['Madhava Krishna', 'Srinath Sridhar', 'Brojeshwar B.', 'Gaurav Singh', 'Aditya Agarwal', 'Bipasha Sen'] | 2023-01-17 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [-1.37181468e-02 2.68633157e-01 -1.59212813e-01 -4.08141166e-01
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1b4fdcb1-954b-4adf-968a-a2203909b144 | towards-creating-a-deployable-grasp-type | 2101.05357 | null | https://arxiv.org/abs/2101.05357v1 | https://arxiv.org/pdf/2101.05357v1.pdf | Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand | For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyography (EMG) signal from residual limb muscles to infer the intended motion. However, limited signal quality, user diversity and high variability adversely affect the system robustness. Instead of solely relying on EMG signals, our work enables augmenting EMG intent inference with physical state probability through machine learning and computer vision method. To this end, we: (1) study state-of-the-art deep neural network architectures to select a performant source of knowledge transfer for the prosthetic hand, (2) use a dataset containing object images and probability distribution of grasp types as a new form of labeling where instead of using absolute values of zero and one as the conventional classification labels, our labels are a set of probabilities whose sum is 1. The proposed method generates probabilistic predictions which could be fused with EMG prediction of probabilities over grasps by using the visual information from the palm camera of a prosthetic hand. Our results demonstrate that InceptionV3 achieves highest accuracy with 0.95 angular similarity followed by 1.4 MobileNetV2 with 0.93 at ~20% the amount of operations. | ['Gunar Schirner', 'Deniz Erdogmus', 'Mo Han', 'Mehrshad Zandigohar'] | 2021-01-13 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.66780943e-01 6.74993694e-02 -4.72379327e-01 1.27397040e-02
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68e91553-b8e2-4ec1-8b02-22ef755f74e2 | on-the-limitations-of-elo-real-world-games | 2206.12301 | null | https://arxiv.org/abs/2206.12301v3 | https://arxiv.org/pdf/2206.12301v3.pdf | On the Limitations of Elo: Real-World Games, are Transitive, not Additive | Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans. | ['Gauthier Gidel', 'Wojciech Marian Czarnecki', 'Quentin Bertrand'] | 2022-06-21 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-1.19557030e-01 1.40904993e-01 4.05375957e-02 3.34076166e-01
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17e4511e-5bd5-4f76-84b6-55a075e63c96 | anomaly-segmentation-model-for-defects | 2208.05994 | null | https://arxiv.org/abs/2208.05994v3 | https://arxiv.org/pdf/2208.05994v3.pdf | Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells | Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt model integration into the industrial landscape. The model has been trained with the on-plant collected dataset consisting of 68 748 electroluminescent images of heterojunction solar cells with a busbar grid. Our model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and precision 96.9% within the validation subset consisting of 1049 manually annotated images. The model was also tested on the open ELPV dataset and demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The SeMaCNN model demonstrates a good balance between its performance and computational costs, which make it applicable for integrating into quality control systems of solar cell manufacturing. | ['Semen Budennyy', 'Leonid Zhukov', 'Igor Shakhray', 'Evgeny Terukov', 'Dmitry Saykin', 'Fedor Egorov', 'Artem Vasilyev', 'Alexey Korovin'] | 2022-08-11 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.53421208e-01 -1.27000913e-01 2.06572667e-01 -1.49911582e-01
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812def89-6893-42ea-a9da-4b43abdbd272 | learning-to-interact-an-adaptive-interaction | null | null | https://aclanthology.org/2020.icon-main.8 | https://aclanthology.org/2020.icon-main.8.pdf | Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings | Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets. | ['Partha Talukdar', 'Nilesh Agrawal', '. Chandrahas'] | null | null | null | null | icon-2020-12 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.99669388e-02 2.41870537e-01 -6.88049078e-01 -3.60973597e-01
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a7322ea0-def7-4446-9586-bb06742e50c6 | image-to-image-translation-with-conditional | 1611.07004 | null | http://arxiv.org/abs/1611.07004v3 | http://arxiv.org/pdf/1611.07004v3.pdf | Image-to-Image Translation with Conditional Adversarial Networks | We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems that traditionally would require very different loss formulations.
We demonstrate that this approach is effective at synthesizing photos from
label maps, reconstructing objects from edge maps, and colorizing images, among
other tasks. Indeed, since the release of the pix2pix software associated with
this paper, a large number of internet users (many of them artists) have posted
their own experiments with our system, further demonstrating its wide
applicability and ease of adoption without the need for parameter tweaking. As
a community, we no longer hand-engineer our mapping functions, and this work
suggests we can achieve reasonable results without hand-engineering our loss
functions either. | ['Jun-Yan Zhu', 'Tinghui Zhou', 'Alexei A. Efros', 'Phillip Isola'] | 2016-11-21 | image-to-image-translation-with-conditional-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf | cvpr-2017-7 | ['fundus-to-angiography-generation', 'cross-view-image-to-image-translation', 'nuclear-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 4.82142985e-01 1.38289943e-01 3.02360952e-01 -5.02346158e-01
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d4047c58-5b18-4354-aa6d-f6d7058018c2 | transfer-cross-modality-knowledge-transfer | 2306.15114 | null | https://arxiv.org/abs/2306.15114v1 | https://arxiv.org/pdf/2306.15114v1.pdf | Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition | Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals. | ['Sandeep K. S. Gupta', 'Ayan Banerjee', 'Payal Kamboj'] | 2023-06-26 | null | null | null | null | ['gesture-recognition', 'transfer-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.12641180e-01 -4.11793590e-01 -3.81419212e-01 -4.35515493e-01
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c108a3c2-6f4d-424a-8c54-8e47c270d6d5 | byt5-towards-a-token-free-future-with-pre | 2105.13626 | null | https://arxiv.org/abs/2105.13626v3 | https://arxiv.org/pdf/2105.13626v3.pdf | ByT5: Towards a token-free future with pre-trained byte-to-byte models | Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. | ['Colin Raffel', 'Adam Roberts', 'Mihir Kale', 'Sharan Narang', 'Rami Al-Rfou', 'Noah Constant', 'Aditya Barua', 'Linting Xue'] | 2021-05-28 | null | null | null | null | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering', 'extreme-summarization', 'cross-lingual-ner'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.71930486e-01 -3.64915490e-01 -2.21152872e-01 -3.08871448e-01
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08bc5cb4-cd78-452f-96c9-a3e8d70f2529 | osre-object-to-spot-rotation-estimation-for | 2303.00725 | null | https://arxiv.org/abs/2303.00725v1 | https://arxiv.org/pdf/2303.00725v1.pdf | OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment | Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations. This paper tackles these two challenges to solve the rotation estimation of a parked bike with respect to its parking area. First, we leverage the power of 3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator (OSRE) by extending the object detection task to further regress the bike rotations in two axes. Since our model is purely trained on synthetic data, we adopt image smoothing techniques when deploying it on real-world images. The proposed OSRE is evaluated on synthetic and real-world data providing promising results. Our data and code are available at \href{https://github.com/saghiralfasly/OSRE-Project}{https://github.com/saghiralfasly/OSRE-Project}. | ['Chen Xu', 'Jian Lu', 'Ahmed Elazab', 'Saifullah Bello', 'Zaid Al-huda', 'Saghir Alfasly'] | 2023-03-01 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [-3.69159013e-01 -1.92377567e-01 -1.44327134e-01 -3.17290813e-01
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4ed6e4c4-7de3-4af0-8b19-3b2bf687592a | defending-model-inversion-and-membership | 2005.03915 | null | https://arxiv.org/abs/2005.03915v2 | https://arxiv.org/pdf/2005.03915v2.pdf | Defending Model Inversion and Membership Inference Attacks via Prediction Purification | Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a unified approach, namely purification framework, to defend data inference attacks. It purifies the confidence score vectors predicted by the target classifier by reducing their dispersion. The purifier can be further specialized in defending a particular attack via adversarial learning. We evaluate our approach on benchmark datasets and classifiers. We show that when the purifier is dedicated to one attack, it naturally defends the other one, which empirically demonstrates the connection between the two attacks. The purifier can effectively defend both attacks. For example, it can reduce the membership inference accuracy by up to 15% and increase the model inversion error by a factor of up to 4. Besides, it incurs less than 0.4% classification accuracy drop and less than 5.5% distortion to the confidence scores. | ['Ee-Chien Chang', 'Ziqi Yang', 'Bin Shao', 'Fan Zhang', 'Bohan Xuan'] | 2020-05-08 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 5.03736496e-01 3.74291360e-01 1.17965274e-01 -3.09128929e-02
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ba7050a2-ada1-4a0a-9f48-8de406c0c266 | multitask-identity-aware-image-steganography | 2107.05819 | null | https://arxiv.org/abs/2107.05819v1 | https://arxiv.org/pdf/2107.05819v1.pdf | Multitask Identity-Aware Image Steganography via Minimax Optimization | High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receiving end. To address this issue, we propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images. The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time. Thus, we introduce a simple content loss to preserve the identity information, and design a minimax optimization to deal with the contradictory aspects. We demonstrate that the robustness results can be transferred across different cover datasets. In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method, providing a multitask framework. The experiments under the multitask scenario show the effectiveness of our framework compared with other visual information hiding methods and state-of-the-art high-capacity image steganography methods. | ['Xi Li', 'Jupeng Xia', 'Cuizhu Bao', 'Liangli Zheng', 'Songyuan Li', 'Pengyi Zhang', 'Jiabao Cui'] | 2021-07-13 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 9.84298527e-01 3.70086581e-02 4.68723476e-02 -1.63694650e-01
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ec3b0c30-902c-4d9d-8aa6-106eeaa32e4e | temporally-consistent-online-depth-estimation | 2111.09337 | null | https://arxiv.org/abs/2111.09337v3 | https://arxiv.org/pdf/2111.09337v3.pdf | Temporally Consistent Online Depth Estimation in Dynamic Scenes | Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising way to generate 3D information, there is relatively little work focused on maintaining temporal stability. Indeed, based on our analysis, current techniques still suffer from poor temporal consistency. Stabilizing depth temporally in dynamic scenes is challenging due to concurrent object and camera motion. In an online setting, this process is further aggravated because only past frames are available. We present a framework named Consistent Online Dynamic Depth (CODD) to produce temporally consistent depth estimates in dynamic scenes in an online setting. CODD augments per-frame stereo networks with novel motion and fusion networks. The motion network accounts for dynamics by predicting a per-pixel SE3 transformation and aligning the observations. The fusion network improves temporal depth consistency by aggregating the current and past estimates. We conduct extensive experiments and demonstrate quantitatively and qualitatively that CODD outperforms competing methods in terms of temporal consistency and performs on par in terms of per-frame accuracy. | ['Mathias Unberath', 'Ganesh Venkatesh', 'Russell H. Taylor', 'Francis X. Creighton', 'Dilin Wang', 'Wei Ye', 'Zhaoshuo Li'] | 2021-11-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.77718514e-01 -2.88761884e-01 -1.27866790e-01 -3.68972927e-01
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aeb6b0ae-49a9-47a5-8aef-886ac18ba591 | nowcasting-of-covid-19-confirmed-cases | 2010.05079 | null | http://arxiv.org/abs/2010.05079v1 | http://arxiv.org/pdf/2010.05079v1.pdf | Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges | The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting more than 200 countries and territories
worldwide. As of September 30, 2020, it has caused a pandemic outbreak with
more than 33 million confirmed infections and more than 1 million reported
deaths worldwide. Several statistical, machine learning, and hybrid models have
previously tried to forecast COVID-19 confirmed cases for profoundly affected
countries. Due to extreme uncertainty and nonstationarity in the time series
data, forecasting of COVID-19 confirmed cases has become a very challenging
job. For univariate time series forecasting, there are various statistical and
machine learning models available in the literature. But, epidemic forecasting
has a dubious track record. Its failures became more prominent due to
insufficient data input, flaws in modeling assumptions, high sensitivity of
estimates, lack of incorporation of epidemiological features, inadequate past
evidence on effects of available interventions, lack of transparency, errors,
lack of determinacy, and lack of expertise in crucial disciplines. This chapter
focuses on assessing different short-term forecasting models that can forecast
the daily COVID-19 cases for various countries. In the form of an empirical
study on forecasting accuracy, this chapter provides evidence to show that
there is no universal method available that can accurately forecast pandemic
data. Still, forecasters' predictions are useful for the effective allocation
of healthcare resources and will act as an early-warning system for government
policymakers. | [] | 2020-10-10 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-5.21630608e-02 -3.79730284e-01 -2.80202538e-01 -5.00902124e-02
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b821d8a7-6d2f-4eba-9fe4-9bf60fe40f8d | expeditious-saliency-guided-mix-up-through | 2212.04875 | null | https://arxiv.org/abs/2212.04875v2 | https://arxiv.org/pdf/2212.04875v2.pdf | Expeditious Saliency-guided Mix-up through Random Gradient Thresholding | Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks. Over the years, the research community expands mix-up methods into two directions, with extensive efforts to improve saliency-guided procedures but minimal focus on the arbitrary path, leaving the randomization domain unexplored. In this paper, inspired by the superior qualities of each direction over one another, we introduce a novel method that lies at the junction of the two routes. By combining the best elements of randomness and saliency utilization, our method balances speed, simplicity, and accuracy. We name our method R-Mix following the concept of "Random Mix-up". We demonstrate its effectiveness in generalization, weakly supervised object localization, calibration, and robustness to adversarial attacks. Finally, in order to address the question of whether there exists a better decision protocol, we train a Reinforcement Learning agent that decides the mix-up policies based on the classifier's performance, reducing dependency on human-designed objectives and hyperparameter tuning. Extensive experiments further show that the agent is capable of performing at the cutting-edge level, laying the foundation for a fully automatic mix-up. Our code is released at [https://github.com/minhlong94/Random-Mixup]. | ['Haohan Wang', 'Yong Jae Lee', 'Eric P. Xing', 'Zeyi Huang', 'Minh-Long Luu'] | 2022-12-09 | null | null | null | null | ['classifier-calibration', 'weakly-supervised-object-localization', 'classifier-calibration'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.25020608e-01 -1.58820301e-02 -3.14871758e-01 -1.98033169e-01
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78fe6682-0e0e-4c32-94c1-c1dc7b1a8ed1 | exploring-content-based-image-retrieval-for | 2110.06331 | null | https://arxiv.org/abs/2110.06331v1 | https://arxiv.org/pdf/2110.06331v1.pdf | Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning | Lesion images are frequently taken in open-set settings. Because of this, the image data generated is extremely varied in nature.It is difficult for a convolutional neural network to find proper features and generalise well, as a result content based image retrieval (CBIR) system for lesion images are difficult to build. This paper explores this domain and proposes multiple similarity measures which uses Style Loss and Dice Coefficient via a novel similarity measure called I1-Score. Out of the CBIR similarity measures proposed, pure style loss approach achieves a remarkable accuracy increase over traditional approaches like Euclidean Distance and Cosine Similarity. The I1-Scores using style loss performed better than traditional approaches by a small margin, whereas, I1-Scores with dice-coefficient faired very poorly. The model used is trained using ensemble learning for better generalization. | ['Priyam Mehta'] | 2021-10-12 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.55804932e-01 -2.75470048e-01 -1.66451577e-02 -3.12285364e-01
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b63496d1-d0ea-410d-85fd-48959424dbfe | differential-diffusion-giving-each-pixel-its | 2306.00950 | null | https://arxiv.org/abs/2306.00950v1 | https://arxiv.org/pdf/2306.00950v1.pdf | Differential Diffusion: Giving Each Pixel Its Strength | Text-based image editing has advanced significantly in recent years. With the rise of diffusion models, image editing via textual instructions has become ubiquitous. Unfortunately, current models lack the ability to customize the quantity of the change per pixel or per image fragment, resorting to changing the entire image in an equal amount, or editing a specific region using a binary mask. In this paper, we suggest a new framework which enables the user to customize the quantity of change for each image fragment, thereby enhancing the flexibility and verbosity of modern diffusion models. Our framework does not require model training or fine-tuning, but instead performs everything at inference time, making it easily applicable to an existing model. We show both qualitatively and quantitatively that our method allows better controllability and can produce results which are unattainable by existing models. Our code is available at: https://github.com/exx8/differential-diffusion | ['Ohad Fried', 'Eran Levin'] | 2023-06-01 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 3.06056261e-01 7.15996921e-02 -1.14231072e-01 -7.08523393e-02
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e814c447-c04b-4fad-a5d8-63a90635d547 | boundary-corrected-multi-scale-fusion-network | 2203.00436 | null | https://arxiv.org/abs/2203.00436v1 | https://arxiv.org/pdf/2203.00436v1.pdf | Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation | Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application. Existing semantic segmentation methods mainly rely on the high-resolution input to achieve high accuracy and do not meet the requirements of inference time. Although some methods focus on high-speed scene parsing with lightweight architectures, they can not fully mine semantic features under low computation with relatively low performance. To realize the real-time and high-precision segmentation, we propose a new method named Boundary Corrected Multi-scale Fusion Network, which uses the designed Low-resolution Multi-scale Fusion Module to extract semantic information. Moreover, to deal with boundary errors caused by low-resolution feature map fusion, we further design an additional Boundary Corrected Loss to constrain overly smooth features. Extensive experiments show that our method achieves a state-of-the-art balance of accuracy and speed for the real-time semantic segmentation. | ['Yidong Li', 'Xu Wang', 'Tengfei Liang', 'Yi Jin', 'Tianjiao Jiang'] | 2022-03-01 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 4.67928499e-01 -2.05736995e-01 -5.15165459e-03 -7.10607171e-01
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5e5340da-e08c-4ae0-a61a-c966a32b2856 | pavel-decorative-patterns-with-packed | 2102.01029 | null | https://arxiv.org/abs/2102.01029v1 | https://arxiv.org/pdf/2102.01029v1.pdf | PAVEL: Decorative Patterns with Packed Volumetric Elements | Many real-world hand-crafted objects are decorated with elements that are packed onto the object's surface and deformed to cover it as much as possible. Examples are artisanal ceramics and metal jewelry. Inspired by these objects, we present a method to enrich surfaces with packed volumetric decorations. Our algorithm works by first determining the locations in which to add the decorative elements and then removing the non-physical overlap between them while preserving the decoration volume. For the placement, we support several strategies depending on the desired overall motif. To remove the overlap, we use an approach based on implicit deformable models creating the qualitative effect of plastic warping while avoiding expensive and hard-to-control physical simulations. Our decorative elements can be used to enhance virtual surfaces, as well as 3D-printed pieces, by assembling the decorations onto real-surfaces to obtain tangible reproductions. | ['Andrea Giachetti', 'Riccardo Scateni', 'Fabio Pellacini', 'Filippo Andrea Fanni'] | 2021-02-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 3.90593737e-01 4.18022633e-01 5.86149156e-01 1.11991741e-01
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451f7235-a5a9-453a-a5f1-888b188c7eaf | rsi-cb-a-large-scale-remote-sensing-image | 1705.10450 | null | https://arxiv.org/abs/1705.10450v3 | https://arxiv.org/pdf/1705.10450v3.pdf | RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data | In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via DCNN, large-scale database (such as ImageNet), and supercomputing ability powered by GPU. The remote sensing field is still lacking a large-scale benchmark compared to ImageNet and Place2. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data. Using crowdsource data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively by points of interest, vector data from OSM, or other crowdsource data. The annotated images can be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has two sub-datasets with 256 by 256 and 128 by 128 sizes because different DCNNs require different image sizes. The former contains 6 categories with 35 subclasses of more than 24,000 images. The latter contains 6 categories with 45 subclasses of more than 36,000 images. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct many experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced datasets on handcrafted features, such as scale-invariant feature transform, color histogram, local binary patterns, and GIST, and classical DCNN models, such as AlexNet, VGGNet, GoogLeNet, and ResNet. | ['Xin Dou', 'Jie Chen', 'Haifeng Li', 'Chao Tao', 'Zhixiang Hou', 'Min Deng', 'Ling Zhao', 'Jian Peng'] | 2017-05-30 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [-3.24394345e-01 -5.14428437e-01 6.98565841e-02 -5.77822864e-01
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4edca1b1-1bdf-47dc-b4d8-f2a317795ff6 | oneshotstl-one-shot-seasonal-trend | 2304.01506 | null | https://arxiv.org/abs/2304.01506v1 | https://arxiv.org/pdf/2304.01506v1.pdf | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data points within a time window. Therefore, they cannot always efficiently support real-time analysis that demands low processing delay. To address this challenge, we propose OneShotSTL, an efficient and accurate algorithm that can decompose time series online with an update time complexity of O(1). OneShotSTL is more than $1,000$ times faster than the batch methods, with accuracy comparable to the best counterparts. Extensive experiments on real-world benchmark datasets for downstream time series anomaly detection and forecasting tasks demonstrate that OneShotSTL is from 10 to over 1,000 times faster than the state-of-the-art methods, while still providing comparable or even better accuracy. | ['Feifei Li', 'Bin Wu', 'Jian Tan', 'Ye Li', 'Xiao He'] | 2023-04-04 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.94721580e-01 -7.03459978e-01 3.88179161e-02 -1.92943737e-01
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3c95bfb6-5dee-47ef-a5f5-3e62de9129f3 | accerl-policy-acceleration-framework-for-deep | 2211.15023 | null | https://arxiv.org/abs/2211.15023v1 | https://arxiv.org/pdf/2211.15023v1.pdf | AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning | Deep reinforcement learning has achieved great success in various fields with its super decision-making ability. However, the policy learning process requires a large amount of training time, causing energy consumption. Inspired by the redundancy of neural networks, we propose a lightweight parallel training framework based on neural network compression, AcceRL, to accelerate the policy learning while ensuring policy quality. Specifically, AcceRL speeds up the experience collection by flexibly combining various neural network compression methods. Overall, the AcceRL consists of five components, namely Actor, Learner, Compressor, Corrector, and Monitor. The Actor uses the Compressor to compress the Learner's policy network to interact with the environment. And the generated experiences are transformed by the Corrector with Off-Policy methods, such as V-trace, Retrace and so on. Then the corrected experiences are feed to the Learner for policy learning. We believe this is the first general reinforcement learning framework that incorporates multiple neural network compression techniques. Extensive experiments conducted in gym show that the AcceRL reduces the time cost of the actor by about 2.0 X to 4.13 X compared to the traditional methods. Furthermore, the AcceRL reduces the whole training time by about 29.8% to 40.3% compared to the traditional methods while keeps the same policy quality. | ['Hongjie Zhang'] | 2022-11-28 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-6.72582462e-02 -1.60217449e-01 -5.65740228e-01 -1.73244804e-01
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0b9bff16-d246-413b-a22c-fd10c8bdec0f | semantic-search-in-documents-enriched-by-lod | null | null | https://aclanthology.org/L14-1040 | https://aclanthology.org/L14-1040.pdf | Semantic Search in Documents Enriched by LOD-based Annotations | This paper deals with information retrieval on semantically enriched web-scale document collections. It particularly focuses on web-crawled content in which mentions of entities appearing in Freebase, DBpedia and other Linked Open Data resources have been identified. A special attention is paid to indexing structures and advanced query mechanisms that have been employed into a new semantic retrieval system. Scalability features are discussed together with performance statistics and results of experimental evaluation of presented approaches. Examples given to demonstrate key features of the developed solution correspond to the cultural heritage domain in which the results of our work have been primarily applied. | ['Jan Kouril', 'Pavel Smrz'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['semantic-retrieval'] | ['natural-language-processing'] | [-2.60269701e-01 3.84891540e-01 7.40839988e-02 -6.21911604e-03
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92c60cda-1d97-4081-a1eb-8623390c3164 | speechformer-a-hierarchical-efficient-1 | 2302.14638 | null | https://arxiv.org/abs/2302.14638v1 | https://arxiv.org/pdf/2302.14638v1.pdf | SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing | Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable success in the natural language processing field and has demonstrated its adaptation to speech. However, previous works on Transformer in the speech field have not incorporated the properties of speech, leaving the full potential of Transformer unexplored. In this paper, we consider the characteristics of speech and propose a general structure-based framework, called SpeechFormer++, for paralinguistic speech processing. More concretely, following the component relationship in the speech signal, we design a unit encoder to model the intra- and inter-unit information (i.e., frames, phones, and words) efficiently. According to the hierarchical relationship, we utilize merging blocks to generate features at different granularities, which is consistent with the structural pattern in the speech signal. Moreover, a word encoder is introduced to integrate word-grained features into each unit encoder, which effectively balances fine-grained and coarse-grained information. SpeechFormer++ is evaluated on the speech emotion recognition (IEMOCAP & MELD), depression classification (DAIC-WOZ) and Alzheimer's disease detection (Pitt) tasks. The results show that SpeechFormer++ outperforms the standard Transformer while greatly reducing the computational cost. Furthermore, it delivers superior results compared to the state-of-the-art approaches. | ['Lan Du', 'Jianxin Pang', 'Xiangmin Xu', 'Xiaofen Xing', 'Weidong Chen'] | 2023-02-27 | null | null | null | null | ['alzheimer-s-disease-detection', 'speech-emotion-recognition'] | ['medical', 'speech'] | [ 1.04144499e-01 -8.62654373e-02 1.21968821e-01 -5.41440308e-01
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21ad340d-ee18-43a4-bbf1-5e61ab980171 | text-compression-for-sentiment-analysis-via | 1709.06990 | null | http://arxiv.org/abs/1709.06990v1 | http://arxiv.org/pdf/1709.06990v1.pdf | Text Compression for Sentiment Analysis via Evolutionary Algorithms | Can textual data be compressed intelligently without losing accuracy in
evaluating sentiment? In this study, we propose a novel evolutionary
compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
which makes use of Parts-of-Speech tags to compress text in a way that
sacrifices minimal classification accuracy when used in conjunction with
sentiment analysis algorithms. An analysis of PARSEC with eight commercial and
non-commercial sentiment analysis algorithms on twelve English sentiment data
sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss
in sentiment classification accuracy for (20%, 50%, 75%) data compression with
PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other
sentiment analysis algorithms are more severely affected by compression. We
conclude that significant compression of text data is possible for sentiment
analysis depending on the accuracy demands of the specific application and the
specific sentiment analysis algorithm used. | ['Emmanuel Dufourq', 'Bruce A. Bassett'] | 2017-09-20 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 5.72681785e-01 -1.06098890e-01 -9.43731517e-02 -6.83626056e-01
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4dcbab14-54af-4a3b-8269-eabaf840d9f8 | marking-anything-application-of-point-cloud | 2306.07559 | null | https://arxiv.org/abs/2306.07559v1 | https://arxiv.org/pdf/2306.07559v1.pdf | Marking anything: application of point cloud in extracting video target features | Extracting retrievable features from video is of great significance for structured video database construction, video copyright protection and fake video rumor refutation. Inspired by point cloud data processing, this paper proposes a method for marking anything (MA) in the video, which can extract the contour features of any target in the video and convert it into a feature vector with a length of 256 that can be retrieved. The algorithm uses YOLO-v8 algorithm, multi-object tracking algorithm and PointNet++ to extract contour of the video detection target to form spatial point cloud data. Then extract the point cloud feature vector and use it as the retrievable feature of the video detection target. In order to verify the effectiveness and robustness of contour feature, some datasets are crawled from Dou Yin and Kinetics-700 dataset as experimental data. For Dou Yin's homogenized videos, the proposed contour features achieve retrieval accuracy higher than 97% in Top1 return mode. For videos from Kinetics 700, the contour feature also showed good robustness for partial clip mode video tracing. | ['Xiangchun Xu'] | 2023-06-13 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.20489398e-01 -9.06444848e-01 -2.19518110e-01 3.58672023e-01
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f563c584-be89-4180-8375-3d74a49759df | structbert-incorporating-language-structures | 1908.04577 | null | https://arxiv.org/abs/1908.04577v3 | https://arxiv.org/pdf/1908.04577v3.pdf | StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding | Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman [8], we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks. The StructBERT with structural pre-training gives surprisingly good empirical results on a variety of downstream tasks, including pushing the state-of-the-art on the GLUE benchmark to 89.0 (outperforming all published models), the F1 score on SQuAD v1.1 question answering to 93.0, the accuracy on SNLI to 91.7. | ['Zuyi Bao', 'Ming Yan', 'Jiangnan Xia', 'Bin Bi', 'Luo Si', 'Liwei Peng', 'Chen Wu', 'Wei Wang'] | 2019-08-13 | null | https://openreview.net/forum?id=BJgQ4lSFPH | https://openreview.net/pdf?id=BJgQ4lSFPH | iclr-2020-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-8.23363289e-02 2.15054020e-01 -3.12488347e-01 -6.20701432e-01
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223463f8-744d-4f0c-be66-c2008dba89ea | transmef-a-transformer-based-multi-exposure | 2112.01030 | null | https://arxiv.org/abs/2112.01030v2 | https://arxiv.org/pdf/2112.01030v2.pdf | TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning | In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-supervised reconstruction tasks according to the characteristics of multi-exposure images and conduct these tasks simultaneously using multi-task learning; through this process, the network can learn the characteristics of multi-exposure images and extract more generalized features. In addition, to compensate for the defect in establishing long-range dependencies in CNN-based architectures, we design an encoder that combines a CNN module with a transformer module. This combination enables the network to focus on both local and global information. We evaluated our method and compared it to 11 competitive traditional and deep learning-based methods on the latest released multi-exposure image fusion benchmark dataset, and our method achieved the best performance in both subjective and objective evaluations. | ['Zhijian Song', 'Manning Wang', 'Shaolei Liu', 'Linhao Qu'] | 2021-12-02 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 2.79846668e-01 -3.47470254e-01 4.42131907e-02 -5.99876881e-01
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fbb66dc4-99f7-439e-8cad-8a2839874abf | mumic-multimodal-embedding-for-multi-label | 2211.05232 | null | https://arxiv.org/abs/2211.05232v1 | https://arxiv.org/pdf/2211.05232v1.pdf | MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid | Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models. We propose the Multimodal Multi-label Image Classification (MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based Binary Cross Entropy loss function, thus enables the optimization on multi-label objectives and transfer learning on CLIP. MuMIC is capable of providing high classification performance, handling real-world noisy data, supporting zero-shot predictions, and producing domain-specific image embeddings. In this study, a total of 120 image classes are defined, and more than 140K positive annotations are collected on approximately 60K Booking.com images. The final MuMIC model is deployed on Booking.com Content Intelligence Platform, and it outperforms other state-of-the-art models with 85.6% GAP@10 and 83.8% GAP on all 120 classes, as well as a 90.1% macro mAP score across 32 majority classes. We summarize the modeling choices which are extensively tested through ablation studies. To the best of our knowledge, we are the first to adapt contrastively learnt multimodal pretraining for real-world multi-label image classification problems, and the innovation can be transferred to other domains. | ['Hadas Harush Boker', 'Karen Lastmann Assaraf', 'Gil Amsalem', 'Guy Nadav', 'Moran Beladev', 'Sarai Mizrachi', 'Fengjun Wang'] | 2022-11-02 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.13845980e-01 -3.52890104e-01 -5.52221298e-01 -4.37814385e-01
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126f5499-537e-46e4-85d4-efb65f20141d | instant-teaching-an-end-to-end-semi | 2103.11402 | null | https://arxiv.org/abs/2103.11402v1 | https://arxiv.org/pdf/2103.11402v1.pdf | Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework | Supervised learning based object detection frameworks demand plenty of laborious manual annotations, which may not be practical in real applications. Semi-supervised object detection (SSOD) can effectively leverage unlabeled data to improve the model performance, which is of great significance for the application of object detection models. In this paper, we revisit SSOD and propose Instant-Teaching, a completely end-to-end and effective SSOD framework, which uses instant pseudo labeling with extended weak-strong data augmentations for teaching during each training iteration. To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$. Extensive experiments on both MS-COCO and PASCAL VOC datasets substantiate the superiority of our framework. Specifically, our method surpasses state-of-the-art methods by 4.2 mAP on MS-COCO when using $2\%$ labeled data. Even with full supervised information of MS-COCO, the proposed method still outperforms state-of-the-art methods by about 1.0 mAP. On PASCAL VOC, we can achieve more than 5 mAP improvement by applying VOC07 as labeled data and VOC12 as unlabeled data. | ['Hao Li', 'Qi Qian', 'Zhibin Wang', 'Chaohui Yu', 'Qiang Zhou'] | 2021-03-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 6.08854182e-02 1.05001293e-01 -1.87817752e-01 -5.62185824e-01
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e8726c8a-f902-4beb-94ac-0d215ed7510b | self-supervised-contrastive-video-speech | 2008.06607 | null | https://arxiv.org/abs/2008.06607v1 | https://arxiv.org/pdf/2008.06607v1.pdf | Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound | In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer. In order to learn meaningful representations, the model needs to identify such correlation and at the same time understand the underlying anatomical features. We designed a framework to model the correspondence between video and audio without any kind of human annotations. Within this framework, we introduce cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling. Experimental evaluations on multi-modal fetal ultrasound video and audio show that the proposed approach is able to learn strong representations and transfers well to downstream tasks of standard plane detection and eye-gaze prediction. | ['Mohammad Alsharid', 'Jianbo Jiao', 'Aris T. Papageorghiou', 'Yifan Cai', 'Lior Drukker', 'J. Alison Noble'] | 2020-08-14 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 3.96199465e-01 7.15805829e-01 1.09377146e-01 -5.22648573e-01
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a9989ccf-1080-492b-b93f-4a59cf30b71f | skipconvgan-monaural-speech-dereverberation | 2211.12623 | null | https://arxiv.org/abs/2211.12623v1 | https://arxiv.org/pdf/2211.12623v1.pdf | SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking | With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements. However, improving the system's robustness against reverberation is still a work in progress, as reverberation tends to cause loss of formant structure due to smearing effects in time and frequency. A wide range of deep learning-based systems either enhance the magnitude response and reuse the distorted phase or enhance complex spectrogram using a complex time-frequency mask. Though these approaches have demonstrated satisfactory performance, they do not directly address the lost formant structure caused by reverberation. We believe that retrieving the formant structure can help improve the efficiency of existing systems. In this study, we propose SkipConvGAN - an extension of our prior work SkipConvNet. The proposed system's generator network tries to estimate an efficient complex time-frequency mask, while the discriminator network aids in driving the generator to restore the lost formant structure. We evaluate the performance of our proposed system on simulated and real recordings of reverberant speech from the single-channel task of the REVERB challenge corpus. The proposed system shows a consistent improvement across multiple room configurations over other deep learning-based generative adversarial frameworks. | ['J. H. L. Hansen', 'Vinay Kothapally'] | 2022-11-22 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.01166643e-01 -3.44071686e-01 7.37222433e-01 -1.57178402e-01
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238bdeab-cf7c-48f0-9909-4ff3e8ee35c6 | learning-from-a-biased-sample | 2209.01754 | null | https://arxiv.org/abs/2209.01754v2 | https://arxiv.org/pdf/2209.01754v2.pdf | Learning from a Biased Sample | The empirical risk minimization approach to data-driven decision making assumes that we can learn a decision rule from training data drawn under the same conditions as the ones we want to deploy it in. However, in a number of settings, we may be concerned that our training sample is biased, and that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented relative to the general population; and in this setting empirical risk minimization over the training set may fail to yield rules that perform well at deployment. We propose a model of sampling bias called $\Gamma$-biased sampling, where observed covariates can affect the probability of sample selection arbitrarily much but the amount of unexplained variation in the probability of sample selection is bounded by a constant factor. Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling. We apply a result of Rockafellar and Uryasev to show that this problem is equivalent to an augmented convex risk minimization problem. We give statistical guarantees for learning a model that is robust to sampling bias via the method of sieves, and propose a deep learning algorithm whose loss function captures our robust learning target. We empirically validate our proposed method in simulations and a case study on ICU length of stay prediction. | ['Stefan Wager', 'Lihua Lei', 'Roshni Sahoo'] | 2022-09-05 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 3.79366517e-01 6.66117609e-01 -5.33854485e-01 -6.30632699e-01
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1bebb5a6-ee50-413a-a790-8f4e024ed3a6 | scatter-based-common-spatial-patterns-a | 2303.06019 | null | https://arxiv.org/abs/2303.06019v1 | https://arxiv.org/pdf/2303.06019v1.pdf | Scatter-based common spatial patterns -- a unified spatial filtering framework | The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification.Therefore, we propose a novel spatial filtering framework called scaCSP based on the scatter matrices of spatial covariances of EEG signals, which works generally in both binary and multi-class problems whereas CSP can be cast into our framework as a special case when only the range space of the between-class scatter matrix is used in binary cases.We further propose subspace enhanced scaCSP algorithms which easily permit incorporating more discriminative information contained in other range spaces and null spaces of the between-class and within-class scatter matrices in two scenarios: a nullspace components reduction scenario and an additional spatial filter learning scenario.The proposed algorithms are evaluated on two data sets including 4 MI tasks. The classification performance is compared against state-of-the-art competing algorithms: CSP, Tikhonov regularized CSP (TRCSP), stationary CSP (sCSP) and stationary TRCSP (sTRCSP) in the binary problems whilst multi-class extensions of CSP based on pair-wise and one-versus-rest techniques in the multi-class problems. The results show that the proposed framework outperforms all the competing algorithms in terms of average classification accuracy and computational efficiency in both binary and multi-class problems.The proposed scsCSP works as a unified framework for general multi-class problems and is promising for improving the performance of MI-BCIs. | ['Jens Haueisen', 'Daniel Baumgarten', 'Johannes Vorwerk', 'Milana Komosar', 'Jinlong Dong'] | 2023-03-07 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.62290019e-01 -7.91650116e-01 1.88289791e-01 1.74462900e-03
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4c3b5521-894f-4f12-86ff-eb2854952534 | how-will-your-tweet-be-received-predicting-1 | 2104.10513 | null | https://arxiv.org/abs/2104.10513v1 | https://arxiv.org/pdf/2104.10513v1.pdf | How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies | Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETWEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step. Second, we use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets. The resulting classifier is evaluated on the new RETWEET dataset, showing promising results, especially considering that it has been trained without any manually labeled data. Both the dataset and the baseline implementation are publicly available. | ['Stefan Evert', 'Mahshad Lotfinia', 'Hamidreza Naderi Boldaji', 'Anguelos Nicolaou', 'Philipp Heinrich', 'Vincent Christlein', 'Mehrpad Monajem', 'Soroosh Tayebi Arasteh'] | 2021-04-21 | how-will-your-tweet-be-received-predicting | https://ieeexplore.ieee.org/document/9364527 | https://ieeexplore.ieee.org/document/9364527 | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.83630422e-01 2.01233089e-01 -2.19412386e-01 -8.18705857e-01
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894ac673-d31b-42de-9b0f-4163a6d96b60 | speaker-extraction-with-co-speech-gestures | 2203.16840 | null | https://arxiv.org/abs/2203.16840v2 | https://arxiv.org/pdf/2203.16840v2.pdf | Speaker Extraction with Co-Speech Gestures Cue | Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speaker cue. In human communication, co-speech gestures that are naturally timed with speech also contribute to speech perception. In this work, we explore the use of co-speech gestures sequence, e.g. hand and body movements, as the speaker cue for speaker extraction, which could be easily obtained from low-resolution video recordings, thus more available than face recordings. We propose two networks using the co-speech gestures cue to perform attentive listening on the target speaker, one that implicitly fuses the co-speech gestures cue in the speaker extraction process, the other performs speech separation first, followed by explicitly using the co-speech gestures cue to associate a separated speech to the target speaker. The experimental results show that the co-speech gestures cue is informative in associating with the target speaker. | ['Haizhou Li', 'Xinyuan Qian', 'Zexu Pan'] | 2022-03-31 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.74032944e-01 2.31574133e-01 -1.04544282e-01 -5.21366298e-01
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e13d3500-2cc1-42cb-87b9-36194c83f990 | training-protocol-matters-towards-accurate | 2203.06696 | null | https://arxiv.org/abs/2203.06696v2 | https://arxiv.org/pdf/2203.06696v2.pdf | Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching | The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models. However, training protocol (i.e., settings of the hyper-parameters involved in the training of STR models), which plays an equally important role in successfully training a good STR model, is under-explored for scene text recognition. In this work, we attempt to improve the accuracy of existing STR models by searching for optimal training protocol. Specifically, we develop a training protocol search algorithm, based on a newly designed search space and an efficient search algorithm using evolutionary optimization and proxy tasks. Experimental results show that our searched training protocol can improve the recognition accuracy of mainstream STR models by 2.7%~3.9%. In particular, with the searched training protocol, TRBA-Net achieves 2.1% higher accuracy than the state-of-the-art STR model (i.e., EFIFSTR), while the inference speed is 2.3x and 3.7x faster on CPU and GPU respectively. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method and the generalization ability of the training protocol found by our search method. Code is available at https://github.com/VDIGPKU/STR_TPSearch. | ['Wei Chu', 'Jingdong Chen', 'Chunhua Shen', 'Yongtao Wang', 'Xiaojie Chu'] | 2022-03-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.07808737e-02 -6.65279686e-01 -1.06522277e-01 -2.60364473e-01
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199be981-55b3-46c5-887e-8bef66e6dd3b | finding-regions-of-heterogeneity-in-decision | 2110.14508 | null | https://arxiv.org/abs/2110.14508v1 | https://arxiv.org/pdf/2110.14508v1.pdf | Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance | Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these examples in mind, we present an algorithm for identifying types of contexts (e.g., types of cases or patients) with high inter-decision-maker disagreement. We formalize this as a causal inference problem, seeking a region where the assignment of decision-maker has a large causal effect on the decision. Our algorithm finds such a region by maximizing an empirical objective, and we give a generalization bound for its performance. In a semi-synthetic experiment, we show that our algorithm recovers the correct region of heterogeneity accurately compared to baselines. Finally, we apply our algorithm to real-world healthcare datasets, recovering variation that aligns with existing clinical knowledge. | ['David Sontag', 'Leora Horwitz', 'Saul Blecker', 'Michael Oberst', 'Christina X Ji', 'Justin Lim'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/81930c54e08b6d26d9638dd2e4656dc1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/81930c54e08b6d26d9638dd2e4656dc1-Paper.pdf | neurips-2021-12 | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.60382491e-01 2.84015805e-01 -9.04438853e-01 -7.03112662e-01
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34f684ec-9501-452c-875e-1c3ff1737b4d | griddehazenet-attention-based-multi-scale | 1908.03245 | null | https://arxiv.org/abs/1908.03245v1 | https://arxiv.org/pdf/1908.03245v1.pdf | GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing | We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by hand-selected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned. | ['Jun Chen', 'Zhihao Shi', 'Yongrui Ma', 'Xiaohong Liu'] | 2019-08-08 | griddehazenet-attention-based-multi-scale-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_GridDehazeNet_Attention-Based_Multi-Scale_Network_for_Image_Dehazing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_GridDehazeNet_Attention-Based_Multi-Scale_Network_for_Image_Dehazing_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-relighting'] | ['computer-vision'] | [ 3.84433568e-01 1.63112998e-01 7.99171329e-01 -3.47883373e-01
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c28dcb02-69fb-4afb-96c7-96282b695fa6 | realistic-speech-driven-facial-animation-with | 1906.06337 | null | https://arxiv.org/abs/1906.06337v1 | https://arxiv.org/pdf/1906.06337v1.pdf | Realistic Speech-Driven Facial Animation with GANs | Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features. Our method generates videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. Our temporal GAN uses 3 discriminators focused on achieving detailed frames, audio-visual synchronization, and realistic expressions. We quantify the contribution of each component in our model using an ablation study and we provide insights into the latent representation of the model. The generated videos are evaluated based on sharpness, reconstruction quality, lip-reading accuracy, synchronization as well as their ability to generate natural blinks. | ['Maja Pantic', 'Konstantinos Vougioukas', 'Stavros Petridis'] | 2019-06-14 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 4.69928980e-01 4.12040830e-01 2.60608971e-01 -4.82424706e-01
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1179f064-1a15-4d9a-8cd9-a2b46a4080ef | auxiliary-tasks-to-boost-biaffine-semantic | null | null | https://aclanthology.org/2022.findings-acl.190 | https://aclanthology.org/2022.findings-acl.190.pdf | Auxiliary tasks to boost Biaffine Semantic Dependency Parsing | The biaffine parser of (CITATION) was successfully extended to semantic dependency parsing (SDP) (CITATION). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens).To circumvent such an independence of decision, while retaining the O(n^2) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs. Experiments on the three English acyclic datasets of SemEval-2015 task 18 (CITATION), and on French deep syntactic cyclic graphs (CITATION) show modest but systematic performance gains on a near-state-of-the-art baseline using transformer-based contextualized representations. This provides a simple and robust method to boost SDP performance. | ['Marie Candito'] | null | null | null | null | findings-acl-2022-5 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 3.67748857e-01 9.74338770e-01 -2.45943457e-01 -7.03126073e-01
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6eaedfcb-d7ff-47fa-8bd2-efa4116a9254 | logsmooth-gradient-concentration-and-tighter | 2002.04121 | null | https://arxiv.org/abs/2002.04121v3 | https://arxiv.org/pdf/2002.04121v3.pdf | Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo | We show that the gradient norm $\|\nabla f(x)\|$ for $x \sim \exp(-f(x))$, where $f$ is strongly convex and smooth, concentrates tightly around its mean. This removes a barrier in the prior state-of-the-art analysis for the well-studied Metropolized Hamiltonian Monte Carlo (HMC) algorithm for sampling from a strongly logconcave distribution. We correspondingly demonstrate that Metropolized HMC mixes in $\tilde{O}(\kappa d)$ iterations, improving upon the $\tilde{O}(\kappa^{1.5}\sqrt{d} + \kappa d)$ runtime of (Dwivedi et. al. '18, Chen et. al. '19) by a factor $(\kappa/d)^{1/2}$ when the condition number $\kappa$ is large. Our mixing time analysis introduces several techniques which to our knowledge have not appeared in the literature and may be of independent interest, including restrictions to a nonconvex set with good conductance behavior, and a new reduction technique for boosting a constant-accuracy total variation guarantee under weak warmness assumptions. This is the first high-accuracy mixing time result for logconcave distributions using only first-order function information which achieves linear dependence on $\kappa$; we also give evidence that this dependence is likely to be necessary for standard Metropolized first-order methods. | ['Kevin Tian', 'Ruoqi Shen', 'Yin Tat Lee'] | 2020-02-10 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 1.89645067e-01 5.38328104e-02 -9.25596803e-02 -2.03326494e-01
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6050a882-30be-48d2-a7a8-db7e606ba12b | local-advantage-networks-for-cooperative | 2112.12458 | null | https://arxiv.org/abs/2112.12458v2 | https://arxiv.org/pdf/2112.12458v2.pdf | Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. Modern MARL methods have focused on finding factorized value functions. While successful, the resulting methods have convoluted network structures. We take a radically different approach and build on the structure of independent Q-learners. Our algorithm LAN leverages a dueling architecture to represent decentralized policies as separate individual advantage functions w.r.t.\ a centralized critic that is cast aside after training. The critic works as a stabilizer that coordinates the learning and to formulate DQN targets. This enables LAN to keep the number of parameters of its centralized network independent in the number of agents, without imposing additional constraints like monotonic value functions. When evaluated on the SMAC, LAN shows SOTA performance overall and scores more than 80\% wins in two super-hard maps where even QPLEX does not obtain almost any wins. Moreover, when the number of agents becomes large, LAN uses significantly fewer parameters than QPLEX or even QMIX. We thus show that LAN's structure forms a key improvement that helps MARL methods remain scalable. | ['Diederik M. Roijers', 'Ann Nowé', 'Mathieu Reymond', 'Raphaël Avalos'] | 2021-12-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.23882806e-01 2.81891197e-01 -2.92746276e-01 3.42830308e-02
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2fb14e1f-bac2-47b1-8b89-8ac8c26fc96d | amodal-intra-class-instance-segmentation-new | 2303.06596 | null | https://arxiv.org/abs/2303.06596v1 | https://arxiv.org/pdf/2303.06596v1.pdf | Amodal Intra-class Instance Segmentation: New Dataset and Benchmark | Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging. Although important for downstream tasks such as robotic grasping systems, the lack of large-scale amodal datasets with detailed annotations makes it difficult to model intra-class occlusions explicitly. This paper introduces a new amodal dataset for image amodal completion tasks, which contains over 255K images of intra-class occlusion scenarios, annotated with multiple masks, amodal bounding boxes, dual order relations and full appearance for instances and background. We also present a point-supervised scheme with layer priors for amodal instance segmentation specifically designed for intra-class occlusion scenarios. Experiments show that our weakly supervised approach outperforms the SOTA fully supervised methods, while our layer priors design exhibits remarkable performance improvements in the case of intra-class occlusion in both synthetic and real images. | ['Krista A. Ehinger', 'Qiuhong Ke', 'Jiayang Ao'] | 2023-03-12 | null | null | null | null | ['amodal-instance-segmentation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 4.43279207e-01 7.27202237e-01 -2.21295625e-01 -6.76195920e-01
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92fc5f2f-8409-4da6-9b07-b38d53fb3880 | recurrent-neural-circuits-for-contour-1 | 2010.15314 | null | https://arxiv.org/abs/2010.15314v1 | https://arxiv.org/pdf/2010.15314v1.pdf | Recurrent neural circuits for contour detection | We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting a classic perceptual illusion, known as the orientation-tilt illusion. Correcting this illusion significantly reduces gamma-net contour detection accuracy by driving it to prefer low-level edges over high-level object boundary contours. Overall, our study suggests that the orientation-tilt illusion is a byproduct of neural circuits that help biological visual systems achieve robust and efficient contour detection, and that incorporating these circuits in artificial neural networks can improve computer vision. | ['Thomas Serre', 'Alekh Ashok', 'Junkyung Kim', 'Drew Linsley'] | 2020-10-29 | recurrent-neural-circuits-for-contour | https://openreview.net/forum?id=H1gB4RVKvB | https://openreview.net/pdf?id=H1gB4RVKvB | iclr-2020-1 | ['contour-detection'] | ['computer-vision'] | [ 4.15341973e-01 1.60836026e-01 1.27260908e-01 -2.47891054e-01
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9727faa4-6607-4b84-b8ea-83ad31876c2d | dap3d-net-where-what-and-how-actions-occur-in | 1602.03346 | null | http://arxiv.org/abs/1602.03346v1 | http://arxiv.org/pdf/1602.03346v1.pdf | DAP3D-Net: Where, What and How Actions Occur in Videos? | Action parsing in videos with complex scenes is an interesting but
challenging task in computer vision. In this paper, we propose a generic 3D
convolutional neural network in a multi-task learning manner for effective Deep
Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase,
action localization, classification and attributes learning can be jointly
optimized on our appearancemotion data via DAP3D-Net. For an upcoming test
video, we can describe each individual action in the video simultaneously as:
Where the action occurs, What the action is and How the action is performed. To
well demonstrate the effectiveness of the proposed DAP3D-Net, we also
contribute a new Numerous-category Aligned Synthetic Action dataset, i.e.,
NASA, which consists of 200; 000 action clips of more than 300 categories and
with 33 pre-defined action attributes in two hierarchical levels (i.e.,
low-level attributes of basic body part movements and high-level attributes
related to action motion). We learn DAP3D-Net using the NASA dataset and then
evaluate it on our collected Human Action Understanding (HAU) dataset.
Experimental results show that our approach can accurately localize, categorize
and describe multiple actions in realistic videos. | ['Ling Shao', 'Li Liu', 'Yi Zhou'] | 2016-02-10 | null | null | null | null | ['action-understanding', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 2.38462314e-01 -1.15728810e-01 -2.98086733e-01 -4.88010049e-01
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03fc5634-e08c-4c92-bcc4-1961431522f6 | visual-speech-recognition | 1409.1411 | null | http://arxiv.org/abs/1409.1411v1 | http://arxiv.org/pdf/1409.1411v1.pdf | Visual Speech Recognition | Lip reading is used to understand or interpret speech without hearing it, a
technique especially mastered by people with hearing difficulties. The ability
to lip read enables a person with a hearing impairment to communicate with
others and to engage in social activities, which otherwise would be difficult.
Recent advances in the fields of computer vision, pattern recognition, and
signal processing has led to a growing interest in automating this challenging
task of lip reading. Indeed, automating the human ability to lip read, a
process referred to as visual speech recognition (VSR) (or sometimes speech
reading), could open the door for other novel related applications. VSR has
received a great deal of attention in the last decade for its potential use in
applications such as human-computer interaction (HCI), audio-visual speech
recognition (AVSR), speaker recognition, talking heads, sign language
recognition and video surveillance. Its main aim is to recognise spoken word(s)
by using only the visual signal that is produced during speech. Hence, VSR
deals with the visual domain of speech and involves image processing,
artificial intelligence, object detection, pattern recognition, statistical
modelling, etc. | ['Ahmad B. A. Hassanat'] | 2014-09-03 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 7.61526287e-01 1.25324965e-01 -1.21890783e-01 -1.54203102e-01
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6a9e4b85-6ea5-47ac-b083-8ffa17c33411 | deep-learning-denoising-for-eog-artifacts | 2009.08809 | null | https://arxiv.org/abs/2009.08809v1 | https://arxiv.org/pdf/2009.08809v1.pdf | Deep learning denoising for EOG artifacts removal from EEG signals | There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues in EEG denoising processes is removing the ocular artifacts where Electrooculographic (EOG), and EEG signals have an overlap in both frequency and time domains. In this paper, we build and train a deep learning model to deal with this challenge and remove the ocular artifacts effectively. In the proposed scheme, we convert each EEG signal to an image to be fed to a U-NET model, which is a deep learning model usually used in image segmentation tasks. We proposed three different schemes and made our U-NET based models learn to purify contaminated EEG signals similar to the process used in the image segmentation process. The results confirm that one of our schemes can achieve a reliable and promising accuracy to reduce the Mean square error between the target signal (Pure EEGs) and the predicted signal (Purified EEGs). | ['Donya Khaledyan', 'Abolfazl Zargari Khuzani', 'Najmeh Mashhadi', 'Morteza Heidari'] | 2020-09-12 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 4.29094076e-01 -2.51037359e-01 6.52435184e-01 -3.73000950e-01
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d54634ee-618e-40f5-92b1-681b3f7bef3b | scientific-document-summarization-via | 1706.03449 | null | http://arxiv.org/abs/1706.03449v1 | http://arxiv.org/pdf/1706.03449v1.pdf | Scientific document summarization via citation contextualization and scientific discourse | The rapid growth of scientific literature has made it difficult for the
researchers to quickly learn about the developments in their respective fields.
Scientific document summarization addresses this challenge by providing
summaries of the important contributions of scientific papers. We present a
framework for scientific summarization which takes advantage of the citations
and the scientific discourse structure. Citation texts often lack the evidence
and context to support the content of the cited paper and are even sometimes
inaccurate. We first address the problem of inaccuracy of the citation texts by
finding the relevant context from the cited paper. We propose three approaches
for contextualizing citations which are based on query reformulation, word
embeddings, and supervised learning. We then train a model to identify the
discourse facets for each citation. We finally propose a method for summarizing
scientific papers by leveraging the faceted citations and their corresponding
contexts. We evaluate our proposed method on two scientific summarization
datasets in the biomedical and computational linguistics domains. Extensive
evaluation results show that our methods can improve over the state of the art
by large margins. | ['Nazli Goharian', 'Arman Cohan'] | 2017-06-12 | null | null | null | null | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.18439007e-01 3.57883543e-01 -5.86945355e-01 1.96014196e-02
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e44240b9-11de-4630-87a8-a246d0dd346f | depth-from-monocular-images-using-a-semi | 1703.03867 | null | http://arxiv.org/abs/1703.03867v3 | http://arxiv.org/pdf/1703.03867v3.pdf | Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture | Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study. | ['H. Javidnia', 'S. Bazrafkan', 'P. Corcoran', 'J. Lemley'] | 2017-03-10 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.06733155e-01 2.07021490e-01 1.59970596e-01 -5.16122699e-01
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42660256-3494-42d5-988c-4cc3a2e9c73d | wasserstein-distances-for-stereo-disparity | 2007.03085 | null | https://arxiv.org/abs/2007.03085v2 | https://arxiv.org/pdf/2007.03085v2.pdf | Wasserstein Distances for Stereo Disparity Estimation | Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes further problems in ambiguous regions around object boundaries. We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values, and a new loss function that is derived from the Wasserstein distance between the true and the predicted distributions. We validate our approach on a variety of tasks, including stereo disparity and depth estimation, and the downstream 3D object detection. Our approach drastically reduces the error in ambiguous regions, especially around object boundaries that greatly affect the localization of objects in 3D, achieving the state-of-the-art in 3D object detection for autonomous driving. Our code will be available at https://github.com/Div99/W-Stereo-Disp. | ['Wei-Lun Chao', 'Divyansh Garg', 'Kilian Q. Weinberger', 'Yan Wang', 'Mark Campbell', 'Bharath Hariharan'] | 2020-07-06 | null | http://proceedings.neurips.cc/paper/2020/hash/fe7ecc4de28b2c83c016b5c6c2acd826-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fe7ecc4de28b2c83c016b5c6c2acd826-Paper.pdf | neurips-2020-12 | ['stereo-depth-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 1.40081942e-01 -6.06004987e-03 5.93227036e-02 -7.01337397e-01
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fc9606c7-e85f-4fdc-861b-65b5fa233ee8 | few-shot-emotion-recognition-in-conversation | 2109.09366 | null | https://arxiv.org/abs/2109.09366v1 | https://arxiv.org/pdf/2109.09366v1.pdf | Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks | Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context.We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones. | ['Chloé Clavel', 'Luce Lefeuvre', 'Hélène Flamein', 'Matthieu Labeau', 'Gaël Guibon'] | 2021-09-20 | null | https://aclanthology.org/2021.emnlp-main.549 | https://aclanthology.org/2021.emnlp-main.549.pdf | emnlp-2021-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.38658419e-01 6.52470961e-02 5.23089990e-02 -6.94373608e-01
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311738bb-4577-417b-8102-2414b87e4696 | the-impact-of-lexical-and-grammatical-1 | 2202.13972 | null | https://arxiv.org/abs/2202.13972v2 | https://arxiv.org/pdf/2202.13972v2.pdf | The impact of lexical and grammatical processing on generating code from natural language | Considering the seq2seq architecture of TranX for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided. The paper highlights the importance of the lexical substitution component in the current natural language to code systems. | ['Benoît Crabbé', 'Nathanaël Beau'] | 2022-02-28 | null | https://aclanthology.org/2022.findings-acl.173 | https://aclanthology.org/2022.findings-acl.173.pdf | findings-acl-2022-5 | ['code-translation'] | ['computer-code'] | [ 3.39542627e-01 4.24884647e-01 -2.84982771e-01 -1.37435406e-01
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4e6a5f41-5f6d-4f0d-8ba6-77976770b72c | vox-e-text-guided-voxel-editing-of-3d-objects | 2303.12048 | null | https://arxiv.org/abs/2303.12048v1 | https://arxiv.org/pdf/2303.12048v1.pdf | Vox-E: Text-guided Voxel Editing of 3D Objects | Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images that convey complex visual concepts. This generative power has more recently been leveraged to perform text-to-3D synthesis. In this work, we present a technique that harnesses the power of latent diffusion models for editing existing 3D objects. Our method takes oriented 2D images of a 3D object as input and learns a grid-based volumetric representation of it. To guide the volumetric representation to conform to a target text prompt, we follow unconditional text-to-3D methods and optimize a Score Distillation Sampling (SDS) loss. However, we observe that combining this diffusion-guided loss with an image-based regularization loss that encourages the representation not to deviate too strongly from the input object is challenging, as it requires achieving two conflicting goals while viewing only structure-and-appearance coupled 2D projections. Thus, we introduce a novel volumetric regularization loss that operates directly in 3D space, utilizing the explicit nature of our 3D representation to enforce correlation between the global structure of the original and edited object. Furthermore, we present a technique that optimizes cross-attention volumetric grids to refine the spatial extent of the edits. Extensive experiments and comparisons demonstrate the effectiveness of our approach in creating a myriad of edits which cannot be achieved by prior works. | ['Hadar Averbuch-Elor', 'Peter Hedman', 'Gal Fiebelman', 'Etai Sella'] | 2023-03-21 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.31292713e-01 2.16660202e-01 1.72547758e-01 -3.28422725e-01
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1684cb28-7a3b-4872-bad9-eaced3106e5f | segformer-simple-and-efficient-design-for | 2105.15203 | null | https://arxiv.org/abs/2105.15203v3 | https://arxiv.org/pdf/2105.15203v3.pdf | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code will be released at: github.com/NVlabs/SegFormer. | ['Ping Luo', 'Jose M. Alvarez', 'Anima Anandkumar', 'Zhiding Yu', 'Wenhai Wang', 'Enze Xie'] | 2021-05-31 | null | http://proceedings.neurips.cc/paper/2021/hash/64f1f27bf1b4ec22924fd0acb550c235-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf | neurips-2021-12 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.24018155e-01 2.91392207e-01 1.15223778e-02 -1.32472172e-01
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6bdcee42-3127-491e-8cef-4f58c89c2978 | anchor-diffusion-for-unsupervised-video-1 | 1910.10895 | null | https://arxiv.org/abs/1910.10895v1 | https://arxiv.org/pdf/1910.10895v1.pdf | Anchor Diffusion for Unsupervised Video Object Segmentation | Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches tend to favour short-term temporal dependencies and are thus prone to accumulating inaccuracies, which cause drift over time. Moreover, simple (static) image segmentation models, alone, can perform competitively against these methods, which further suggests that the way temporal dependencies are modelled should be reconsidered. Motivated by these observations, in this paper we explore simple yet effective strategies to model long-term temporal dependencies. Inspired by the non-local operators of [70], we introduce a technique to establish dense correspondences between pixel embeddings of a reference "anchor" frame and the current one. This allows the learning of pairwise dependencies at arbitrarily long distances without conditioning on intermediate frames. Without online supervision, our approach can suppress the background and precisely segment the foreground object even in challenging scenarios, while maintaining consistent performance over time. With a mean IoU of $81.7\%$, our method ranks first on the DAVIS-2016 leaderboard of unsupervised methods, while still being competitive against state-of-the-art online semi-supervised approaches. We further evaluate our method on the FBMS dataset and the ViSal video saliency dataset, showing results competitive with the state of the art. | ['Philip H. S. Torr', 'Zhao Yang', 'Weiming Hu', 'Song Bai', 'Qiang Wang', 'Luca Bertinetto'] | 2019-10-24 | anchor-diffusion-for-unsupervised-video | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 4.47090238e-01 4.48896065e-02 -3.42236787e-01 -1.73815474e-01
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7d86d25d-9963-4a98-9ac8-e1ebf0adbcac | maxim-multi-axis-mlp-for-image-processing | 2201.02973 | null | https://arxiv.org/abs/2201.02973v2 | https://arxiv.org/pdf/2201.02973v2.pdf | MAXIM: Multi-Axis MLP for Image Processing | Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for low-level vision. The inflexibility to support high-resolution images and limitations of local attention are perhaps the main bottlenecks. In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks. MAXIM uses a UNet-shaped hierarchical structure and supports long-range interactions enabled by spatially-gated MLPs. Specifically, MAXIM contains two MLP-based building blocks: a multi-axis gated MLP that allows for efficient and scalable spatial mixing of local and global visual cues, and a cross-gating block, an alternative to cross-attention, which accounts for cross-feature conditioning. Both these modules are exclusively based on MLPs, but also benefit from being both global and `fully-convolutional', two properties that are desirable for image processing. Our extensive experimental results show that the proposed MAXIM model achieves state-of-the-art performance on more than ten benchmarks across a range of image processing tasks, including denoising, deblurring, deraining, dehazing, and enhancement while requiring fewer or comparable numbers of parameters and FLOPs than competitive models. The source code and trained models will be available at \url{https://github.com/google-research/maxim}. | ['Yinxiao Li', 'Alan Bovik', 'Peyman Milanfar', 'Feng Yang', 'Han Zhang', 'Hossein Talebi', 'Zhengzhong Tu'] | 2022-01-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-dehazing', 'photo-retouching', 'single-image-deraining'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.88453048e-01 -2.65318990e-01 -2.22724508e-02 -3.68500829e-01
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ec93eee0-6625-4aa8-a5a1-762de8e9db1f | self-supervised-feature-learning-by-cross | 2004.05749 | null | https://arxiv.org/abs/2004.05749v1 | https://arxiv.org/pdf/2004.05749v1.pdf | Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences | The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network. Two types of features are fed into a two-layer fully connected neural network to estimate the cross-modality correspondence. The three networks are jointly trained (i.e. cross-modality) by verifying whether two sampled data of different modalities belong to the same object, meanwhile, the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views (i.e. cross-view). The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape retrieval, and 3D part-segmentation. Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method. | ['Yu-cheng Chen', 'YingLi Tian', 'Mingyi He', 'Longlong Jing', 'Ling Zhang'] | 2020-04-13 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.06332608e-02 6.14980869e-02 -1.93115070e-01 -6.91775441e-01
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cbff6161-e8a7-4b3d-8c10-f0aeea0f387c | graph-aware-language-model-pre-training-on-a | 2306.02592 | null | https://arxiv.org/abs/2306.02592v1 | https://arxiv.org/pdf/2306.02592v1.pdf | Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications | Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be drawn for pre-training graph models on large graphs in the hope of benefiting downstream graph applications, which has also been explored by several recent studies. However, no existing study has ever investigated the pre-training of text plus graph models on large heterogeneous graphs with abundant textual information (a.k.a. large graph corpora) and then fine-tuning the model on different related downstream applications with different graph schemas. To address this problem, we propose a framework of graph-aware language model pre-training (GALM) on a large graph corpus, which incorporates large language models and graph neural networks, and a variety of fine-tuning methods on downstream applications. We conduct extensive experiments on Amazon's real internal datasets and large public datasets. Comprehensive empirical results and in-depth analysis demonstrate the effectiveness of our proposed methods along with lessons learned. | ['Trishul Chilimbi', 'Belinda Zeng', 'Yi Xu', 'Carl Yang', 'Sheng Wang', 'Qing Ping', 'Xiang Song', 'Vassilis N. Ioannidis', 'Houyu Zhang', 'Jun Ma', 'Da Zheng', 'Han Xie'] | 2023-06-05 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.79874286e-01 6.58058107e-01 -4.82638538e-01 -3.41074169e-01
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0508ef62-b3ee-4ba7-9fce-082dab9e9102 | network-aware-5g-edge-computing-for-object | 2112.13194 | null | https://arxiv.org/abs/2112.13194v2 | https://arxiv.org/pdf/2112.13194v2.pdf | Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster | Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing to the cloud. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection within a powerful, new smart wearable called VIS4ION, for the Blind-and-Visually Impaired (BVI). The current VIS4ION system is an instrumented book-bag with high-resolution cameras, vision processing and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge cloud to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and realistic assessment of edge computing with mmWave connectivity in an application with both high bandwidth and low latency requirements. | ['J. R. Rizzo', 'Yao Wang', 'Sundeep Rangan', 'Yi Fang', 'William Seiple', 'Todd Hudson', 'Maurizio Porfiri', 'Mahya Beheshti', 'Marco Mezzavilla', 'Alain Boldini', 'Haoyang Pei', 'Yixuan Lyu', 'Yu Hao', 'Tommy Azzino', 'Zhongzheng Yuan'] | 2021-12-25 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 9.45712551e-02 -4.01366234e-01 1.27299845e-01 1.82529032e-01
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0216c49e-73a0-47be-a4aa-eb954dbe362a | knowledge-enhanced-model-for-live-video | 2304.14657 | null | https://arxiv.org/abs/2304.14657v1 | https://arxiv.org/pdf/2304.14657v1.pdf | Knowledge Enhanced Model for Live Video Comment Generation | Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus on short video datasets while ignoring other important video types such as long videos like movies. In this work, we collect a new Movie Live Comments (MovieLC) dataset to support research on live video comment generation for long videos. We also propose a knowledge enhanced generation model inspired by the divergent and informative nature of live video comments. Our model adopts a pre-training encoder-decoder framework and incorporates external knowledge. Extensive experiments show that both objective metrics and human evaluation demonstrate the effectiveness of our proposed model. The MovieLC dataset and our code will be released. | ['Qin Jin', 'Wenping Chen', 'Junkai Ding', 'Jieting Chen'] | 2023-04-28 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 8.44836235e-02 -5.66304885e-02 -3.92746806e-01 -3.50049883e-01
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7.98702717e-01 -2.47355819e-01 -5.96985996e-01 -5.46318829e-01
6.05595887e-01 3.60970110e-01 1.99803308e-01 -7.43952215e-01
8.27744007e-02 -2.21025363e-01 -2.56471753e-01 -2.50993341e-01
2.05325365e-01 -4.44622010e-01 -1.61235929e-01 1.55156061e-01
-8.82487893e-01 1.98040724e-01 -2.99447954e-01 8.05462658e-01
-5.12138426e-01 -2.64975160e-01 3.60305309e-01 2.67654192e-02
-5.71680546e-01 4.09405977e-01 -8.52667451e-01 3.01896513e-01
1.11427641e+00 -2.06099167e-01 -5.86406887e-01 -1.33841181e+00
-5.36278188e-01 3.22214901e-01 5.33785820e-01 8.70949566e-01
8.52165461e-01 -1.41734397e+00 -8.99369121e-01 -6.18390627e-02
3.58565927e-01 -4.69133586e-01 4.61845160e-01 9.55009878e-01
-7.82784283e-01 2.69412667e-01 -1.33163109e-01 -4.15320188e-01
-1.38486242e+00 4.42080468e-01 -2.99842775e-01 8.44343826e-02
-6.08593404e-01 1.05343425e+00 5.37668504e-02 1.60048138e-02
3.38454604e-01 -1.44230789e-02 -3.44355792e-01 1.21142669e-02
1.23996854e+00 5.05190551e-01 -3.86673599e-01 -7.68381774e-01
9.66140702e-02 -3.74947876e-01 -2.46705353e-01 -4.39787209e-02
1.14137113e+00 -7.19165385e-01 1.02958634e-01 5.63731492e-01
1.34308898e+00 2.06098482e-01 -1.36713314e+00 -6.48706704e-02
-3.88325036e-01 -9.47890401e-01 -3.55874568e-01 -3.45600098e-01
-1.25677693e+00 9.76775765e-01 -5.86058646e-02 3.44041348e-01
8.42133522e-01 -8.14109389e-03 1.40353465e+00 3.31220716e-01
1.47426143e-01 -1.11182368e+00 8.84279311e-01 6.30189478e-01
8.83716285e-01 -1.36050749e+00 -4.56810057e-01 -3.57461452e-01
-1.26056921e+00 1.13692093e+00 7.57818460e-01 7.02550635e-02
5.00186741e-01 1.02615654e-01 2.61495680e-01 1.91080242e-01
-1.36885762e+00 1.68786794e-01 5.34439832e-03 5.93152881e-01
8.93019676e-01 -1.79147184e-01 -4.95693684e-01 5.90079486e-01
-9.08702984e-02 3.78713608e-01 1.27640235e+00 8.56769919e-01
-4.02451813e-01 -1.04146481e+00 1.02695733e-01 4.24877465e-01
-6.85295165e-01 -2.78076649e-01 -4.10467386e-01 1.35801826e-02
-2.20991746e-01 1.43534029e+00 4.86163162e-02 -6.14133537e-01
-3.57890755e-01 -1.50999665e-01 1.22160174e-01 -7.87560463e-01
-8.94294202e-01 -3.21044885e-02 3.99116576e-01 -4.69879806e-01
-8.29675853e-01 -5.15249312e-01 -9.44866002e-01 -6.21501863e-01
-5.48895001e-01 3.58928829e-01 3.40715200e-01 7.75063813e-01
5.09742498e-01 3.21774065e-01 1.00553036e+00 -1.01483083e+00
-7.55647793e-02 -1.13389814e+00 -3.84481490e-01 6.34414375e-01
2.88346767e-01 -1.90606862e-01 -4.07490671e-01 8.41352224e-01] | [10.670393943786621, 0.6611711382865906] |
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