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3e170fa0-369b-4504-969a-e7d9c95323d2 | classification-of-cross-cultural-news-events | 2301.05543 | null | https://arxiv.org/abs/2301.05543v1 | https://arxiv.org/pdf/2301.05543v1.pdf | Classification of Cross-cultural News Events | We present a methodology to support the analysis of culture from text such as news events and demonstrate its usefulness on categorizing news events from different categories (society, business, health, recreation, science, shopping, sports, arts, computers, games and home) across different geographical locations (different places in 117 countries). We group countries based on the culture that they follow and then filter the news events based on their content category. The news events are automatically labelled with the help of Hofstedes cultural dimensions. We present combinations of events across different categories and check the performances of different classification methods. We also presents experimental comparison of different number of features in order to find a suitable set to represent the culture. | ['Dunja Mladenic', 'Abdul Sittar'] | 2023-01-13 | null | null | null | null | ['culture'] | ['speech'] | [-0.42266774 -0.31742427 -0.17943846 -0.13492309 -0.2720359 -0.98806655
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11add1be-ec95-4a6e-aeb4-ab0fa37ae174 | resetox-re-learning-attention-weights-for | 2305.11761 | null | https://arxiv.org/abs/2305.11761v1 | https://arxiv.org/pdf/2305.11761v1.pdf | ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation | Our proposed method, ReSeTOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, ReSeTOX dynamically adjusts the key-value self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that ReSeTOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. | ['Marta R. Costa-jussà', 'Carlos Escolano', 'Javier García Gilabert'] | 2023-05-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 6.63756728e-01 1.04923993e-01 -1.38167620e-01 9.77284238e-02
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1afa3802-2f07-41b5-a060-93ac552f23c9 | machine-learning-assisted-quantum-state | 2003.03441 | null | https://arxiv.org/abs/2003.03441v1 | https://arxiv.org/pdf/2003.03441v1.pdf | Machine learning assisted quantum state estimation | We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. Further, by training our system with measurement results that include simulated noise sources we are able to demonstrate a significantly enhanced average fidelity when compared to typical reconstruction methods. These enhancements in average fidelity are also shown to persist when we consider state reconstruction from partial tomography data where several measurements are missing. We anticipate that the present results combining the fields of machine intelligence and quantum state estimation will greatly improve and speed up tomography-based quantum experiments. | ['Sanjaya Lohani', 'Brian T. Kirby', 'Ryan T. Glasser', 'Onur Danaci', 'Michael Brodsky'] | 2020-03-06 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.57896912e-01 -2.20377937e-01 1.34696037e-01 -3.86873245e-01
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bf916c23-86f8-478b-8b25-347b40566b3c | a-simple-transformer-based-model-for-ego4d | 2211.08704 | null | https://arxiv.org/abs/2211.08704v1 | https://arxiv.org/pdf/2211.08704v1.pdf | A Simple Transformer-Based Model for Ego4D Natural Language Queries Challenge | This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge. Our solution inherits the point-based event representation from our prior work on temporal action localization, and develops a Transformer-based model for video grounding. Further, our solution integrates several strong video features including SlowFast, Omnivore and EgoVLP. Without bells and whistles, our submission based on a single model achieves 12.64% Mean R@1 and is ranked 2nd on the public leaderboard. Meanwhile, our method garners 28.45% (18.03%) R@5 at tIoU=0.3 (0.5), surpassing the top-ranked solution by up to 5.5 absolute percentage points. | ['Yin Li', 'Fangzhou Mu', 'Sicheng Mo'] | 2022-11-16 | null | null | null | null | ['video-grounding', 'action-localization'] | ['computer-vision', 'computer-vision'] | [-4.40448105e-01 -4.76452224e-02 -5.79882979e-01 -5.73557764e-02
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0a4a04b4-79b6-4483-964c-7447a31f9e23 | learning-to-adapt-to-online-streams-with | 2303.01630 | null | https://arxiv.org/abs/2303.01630v1 | https://arxiv.org/pdf/2303.01630v1.pdf | Learning to Adapt to Online Streams with Distribution Shifts | Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference. In this work, we expand TTA to a more practical scenario, where the test data comes in the form of online streams that experience distribution shifts over time. Existing approaches face two challenges: reliance on a large test data batch from the same domain and the absence of explicitly modeling the continual distribution evolution process. To address both challenges, we propose a meta-learning approach that teaches the network to adapt to distribution-shifting online streams during meta-training. As a result, the trained model can perform continual adaptation to distribution shifts in testing, regardless of the batch size restriction, as it has learned during training. We conducted extensive experiments on benchmarking datasets for TTA, incorporating a broad range of online distribution-shifting settings. Our results showed consistent improvements over state-of-the-art methods, indicating the effectiveness of our approach. In addition, we achieved superior performance in the video segmentation task, highlighting the potential of our method for real-world applications. | ['James Z. Wang', 'Yandong Li', 'Yimu Pan', 'Chenyan Wu'] | 2023-03-02 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 2.46977463e-01 -3.96965623e-01 -3.94215286e-01 -6.38443470e-01
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f86b643d-2393-4f9f-b787-e48a31f03d92 | irt2-inductive-linking-and-ranking-in | 2301.00716 | null | https://arxiv.org/abs/2301.00716v1 | https://arxiv.org/pdf/2301.00716v1.pdf | IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale | We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph. We argue that - though neural approaches to text mining have yielded impressive results in the past years - current benchmarks do not reflect the typical challenges encountered in the industrial wild properly. Therefore, our first contribution is an open benchmark coined IRT2 (inductive reasoning with text) that (1) covers knowledge graphs of varying sizes (including very small ones), (2) comes with incidental, low-quality text mentions, and (3) includes not only triple completion but also ranking, which is relevant for supporting experts with discovery tasks. We investigate two neural models for inductive link prediction, one based on end-to-end learning and one that learns from the knowledge graph and text data in separate steps. These models compete with a strong bag-of-words baseline. The results show a significant advance in performance for the neural approaches as soon as the available graph data decreases for linking. For ranking, the results are promising, and the neural approaches outperform the sparse retriever by a wide margin. | ['Maurice Falk', 'Adrian Ulges', 'Felix Hamann'] | 2023-01-02 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [ 1.54390678e-01 6.72193110e-01 -7.84267247e-01 5.97520284e-02
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c4a40746-7c02-4031-9895-77f7bd962b8c | off-grid-direction-of-arrival-estimation | 2112.05487 | null | https://arxiv.org/abs/2112.05487v3 | https://arxiv.org/pdf/2112.05487v3.pdf | Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation | The problem of off-grid direction-of-arrival (DOA) estimation is investigated. We develop a grid-based method to jointly estimate the closest spatial frequency (the sine of DOA) grids, and the gaps between the estimated grids and the corresponding frequencies. By using a second-order Taylor approximation, the data model under the framework of joint-sparse representation is formulated. We point out an important property of the signals of interest in the model, namely the proportionality relationship, which is empirically demonstrated to be useful in the sense that it increases the probability of the mixing matrix satisfying the block restricted isometry property. Simulation examples demonstrate the effectiveness and superiority of the proposed method against several state-of-the-art grid-based approaches. | ['Abdelhak M. Zoubir', 'Hing Cheung So', 'Huiping Huang'] | 2021-12-10 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-1.77350715e-01 -3.51524502e-01 -7.92571530e-02 2.00668260e-01
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8739340d-81df-40f0-bafc-ece07aa75f73 | random-forests-and-vgg-net-an-algorithm-for | 1703.05148 | null | http://arxiv.org/abs/1703.05148v1 | http://arxiv.org/pdf/1703.05148v1.pdf | Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge | This manuscript briefly describes an algorithm developed for the ISIC 2017
Skin Lesion Classification Competition. In this task, participants are asked to
complete two independent binary image classification tasks that involve three
unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis).
In the first binary classification task, participants are asked to distinguish
between (a) melanoma and (b) nevus and seborrheic keratosis. In the second
binary classification task, participants are asked to distinguish between (a)
seborrheic keratosis and (b) nevus and melanoma. The other phases of the
competition are not considered. Our proposed algorithm consists of three steps:
preprocessing, classification using VGG-NET and Random Forests, and calculation
of a final score. | ['Yixin Luo', 'Yanzhi Song', 'Songtao Guo'] | 2017-03-15 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.27448905e-01 -1.51167020e-01 -2.87378162e-01 -2.77033538e-01
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3c79914a-ac85-48d0-942e-42c8375fca56 | ethics-and-deep-learning | 2305.15239 | null | https://arxiv.org/abs/2305.15239v2 | https://arxiv.org/pdf/2305.15239v2.pdf | Deep Learning and Ethics | This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations, militarization, fraud, and environmental concerns. The aim is not to provide advice on being more ethical. Instead, the goal is to express ideas and start conversations in key areas that have received attention in philosophy, political science, and the broader social sciences. | ['Simon J. D. Prince', 'Travis LaCroix'] | 2023-05-24 | null | null | null | null | ['ethics', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [-7.74972066e-02 8.01391840e-01 -3.60217124e-01 -4.97721940e-01
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d69416ae-d280-467e-b010-ebcd5e1b8239 | simultaneously-short-and-long-term-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lao_Simultaneously_Short-_and_Long-Term_Temporal_Modeling_for_Semi-Supervised_Video_Semantic_CVPR_2023_paper.pdf | Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation | In order to tackle video semantic segmentation task at a lower cost, e.g., only one frame annotated per video, lots of efforts have been devoted to investigate the utilization of those unlabeled frames by either assigning pseudo labels or performing feature enhancement. In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation. Compared with existing work that only leverage short-term correspondence, the long-term temporal correlation obtained from distant frames can effectively expand the temporal perception field and provide richer contextual prior. More importantly, modeling adjacent and distant frames together can alleviate the risk of over-fitting, hence produce high-quality feature representation for the distant unlabeled frames in training set and unseen videos in testing set. To this end, we term our method SSLTM, short for Simultaneously Short- and Long-Term Temporal Modeling. In the setting of only one frame annotated per video, SSLTM significantly outperforms the state-of-the-art methods by 2% 3% mIoU on the challenging VSPW dataset. Furthermore, when working with a pseudo label based method such as MeanTeacher, our final model only exhibits 0.13% mIoU less than the ceiling performance (i.e., all frames are manually annotated). | ['Wei Chu', 'Jingdong Chen', 'Jian Wang', 'Yingying Zhang', 'Xin Guo', 'Weixiang Hong', 'Jiangwei Lao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-semantic-segmentation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 2.59117812e-01 3.75253260e-02 -3.64217669e-01 -4.59975243e-01
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37e7a5e9-7876-486a-afe9-d7a1c337284f | dual-accuracy-quality-driven-neural-network | 2212.06370 | null | https://arxiv.org/abs/2212.06370v2 | https://arxiv.org/pdf/2212.06370v2.pdf | Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation | Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep learning models. Such PIs are useful or "high-quality" as long as they are sufficiently narrow and capture most of the probability density. In this paper, we present a method to learn prediction intervals for regression-based neural networks automatically in addition to the conventional target predictions. In particular, we train two companion neural networks: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean prediction interval width and ensuring the PI integrity using constraints that maximize the prediction interval probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher-quality PIs. | ['John W. Sheppard', 'Giorgio Morales'] | 2022-12-13 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 7.49805719e-02 3.74779731e-01 -4.29148108e-01 -6.45985663e-01
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a27e80ec-22e4-4d20-af6f-47dca96151f4 | a-data-driven-rutting-depth-short-time | 2305.06707 | null | https://arxiv.org/abs/2305.06707v1 | https://arxiv.org/pdf/2305.06707v1.pdf | A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack | Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. | ['Jinde Cao', 'Wei Huang', 'Nadezhda Gorbacheva', 'Sergey Gorbachev', 'Xinli Shi', 'Iakov Korovin', 'Zhuoxuan Li'] | 2023-05-11 | null | null | null | null | ['community-detection', 'metaheuristic-optimization'] | ['graphs', 'methodology'] | [-2.07781971e-01 -1.19975777e-02 2.76432544e-01 4.50633699e-03
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10c5a0cc-0a32-479a-bfd6-336a897be73f | monotonic-value-function-factorisation-for | 2003.08839 | null | https://arxiv.org/abs/2003.08839v2 | https://arxiv.org/pdf/2003.08839v2.pdf | Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a mixing network that estimates joint action-values as a monotonic combination of per-agent values. We structurally enforce that the joint-action value is monotonic in the per-agent values, through the use of non-negative weights in the mixing network, which guarantees consistency between the centralised and decentralised policies. To evaluate the performance of QMIX, we propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning. We evaluate QMIX on a challenging set of SMAC scenarios and show that it significantly outperforms existing multi-agent reinforcement learning methods. | ['Tabish Rashid', 'Shimon Whiteson', 'Jakob Foerster', 'Mikayel Samvelyan', 'Gregory Farquhar', 'Christian Schroeder de Witt'] | 2020-03-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.51009631e-01 6.74215183e-02 -4.04609442e-01 -8.89216363e-02
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69379e36-ded1-4f66-a874-1507bcb0921c | submarine-cable-network-design-for-regional | 2201.05802 | null | https://arxiv.org/abs/2201.05802v1 | https://arxiv.org/pdf/2201.05802v1.pdf | Submarine Cable Network Design for Regional Connectivity | This paper optimizes path planning for a trunkand-branch topology network in an irregular 2-dimensional manifold embedded in 3-dimensional Euclidean space with application to submarine cable network planning. We go beyond our earlier focus on the costs of cable construction (including labor, equipment and materials) together with additional cost to enhance cable resilience, to incorporate the overall cost of branching units (again including material, construction and laying) and the choice of submarine cable landing stations, where such a station can be anywhere on the coast in a connected region. These are important issues for the economics of cable laying and significantly change the model and the optimization process. We pose the problem as a variant of the Steiner tree problem, but one in which the Steiner nodes can vary in number, while incurring a penalty. We refer to it as the weighted Steiner node problem. It differs from the Euclidean Steiner tree problem, where Steiner points are forced to have degree three; this is no longer the case, in general, when nodes incur a cost. We are able to prove that our algorithm is applicable to Steiner nodes with degree greater than three, enabling optimization of network costs in this context. The optimal solution is achieved in polynomialtime using dynamic programming. | ['Moshe Zukerman', 'Bill Moran', 'Zengfu Wang', 'Tianjiao Wang'] | 2022-01-15 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 1.59181327e-01 5.14251113e-01 -1.30038381e-01 1.43272907e-01
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afdcbfde-d4b6-4c8f-a918-5b617f761b1e | this-is-not-the-texture-you-are-looking-for | 2012.11905 | null | https://arxiv.org/abs/2012.11905v3 | https://arxiv.org/pdf/2012.11905v3.pdf | GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning | With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especially for image classification tasks, many state-of-the-art tools to explain such classifiers rely on visual highlighting of important areas of the input data. Contrary, counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image in a way such that the classifier would have made a different prediction. By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information. However, methods for generating realistic counterfactual explanations for image classifiers are still rare. Especially in medical contexts, where relevant information often consists of textural and structural information, high-quality counterfactual images have the potential to give meaningful insights into decision processes. In this work, we present GANterfactual, an approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Additionally, we conduct a user study to evaluate our approach in an exemplary medical use case. Our results show that, in the chosen medical use-case, counterfactual explanations lead to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the-art systems that work with saliency maps, namely LIME and LRP. | ['Elisabeth André', 'Alexander Heimerl', 'Katharina Weitz', 'Tobias Huber', 'Silvan Mertes'] | 2020-12-22 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.98384881e-01 9.56184089e-01 -1.74949482e-01 -2.65941381e-01
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0608de78-44bc-4c98-a56e-1ac7c40ac4c3 | learning-by-inertia-self-supervised-monocular | 1905.01634 | null | https://arxiv.org/abs/1905.01634v1 | https://arxiv.org/pdf/1905.01634v1.pdf | Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles | In this paper, we present iDVO (inertia-embedded deep visual odometry), a self-supervised learning based monocular visual odometry (VO) for road vehicles. When modelling the geometric consistency within adjacent frames, most deep VO methods ignore the temporal continuity of the camera pose, which results in a very severe jagged fluctuation in the velocity curves. With the observation that road vehicles tend to perform smooth dynamic characteristics in most of the time, we design the inertia loss function to describe the abnormal motion variation, which assists the model to learn the consecutiveness from long-term camera ego-motion. Based on the recurrent convolutional neural network (RCNN) architecture, our method implicitly models the dynamics of road vehicles and the temporal consecutiveness by the extended Long Short-Term Memory (LSTM) block. Furthermore, we develop the dynamic hard-edge mask to handle the non-consistency in fast camera motion by blocking the boundary part and which generates more efficiency in the whole non-consistency mask. The proposed method is evaluated on the KITTI dataset, and the results demonstrate state-of-the-art performance with respect to other monocular deep VO and SLAM approaches. | ['Qi. Wang', 'Chengze Wang', 'Yuan Yuan'] | 2019-05-05 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-6.78005338e-01 -1.81671396e-01 -5.06371975e-01 -2.28561819e-01
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b852c726-0dc4-4b8a-884c-47277e01b676 | color-mismatches-in-stereoscopic-video-real | 2303.06657 | null | https://arxiv.org/abs/2303.06657v2 | https://arxiv.org/pdf/2303.06657v2.pdf | Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method | We propose a real-world dataset of stereoscopic videos for color-mismatch correction. It includes real-world distortions achieved using a beam splitter. Our dataset is larger than any other for this task. We compared eight color-mismatch-correction methods on artificial and real-world datasets and showed that local methods are best suited to artificial distortions and that global methods are best suited to real-world distortions. Our efforts improved on the latest local neural-network method for color-mismatch correction in stereoscopic images, making it work faster and better on both artificial and real-world distortions. | ['Dmitriy Vatolin', 'Maxim Velikanov', 'Nikita Alutis', 'Egor Chistov'] | 2023-03-12 | null | null | null | null | ['color-mismatch-correction'] | ['computer-vision'] | [ 3.06215972e-01 -6.80356622e-01 1.63410783e-01 -2.74865508e-01
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9b3ff13d-d45a-4e20-84e9-6c64547db122 | unsupervised-steganalysis-based-on-artificial | 1703.00796 | null | http://arxiv.org/abs/1703.00796v1 | http://arxiv.org/pdf/1703.00796v1.pdf | Unsupervised Steganalysis Based on Artificial Training Sets | In this paper, an unsupervised steganalysis method that combines artificial
training setsand supervised classification is proposed. We provide a formal
framework for unsupervisedclassification of stego and cover images in the
typical situation of targeted steganalysis (i.e.,for a known algorithm and
approximate embedding bit rate). We also present a completeset of experiments
using 1) eight different image databases, 2) image features based on
RichModels, and 3) three different embedding algorithms: Least Significant Bit
(LSB) matching,Highly undetectable steganography (HUGO) and Wavelet Obtained
Weights (WOW). Weshow that the experimental results outperform previous methods
based on Rich Models inthe majority of the tested cases. At the same time, the
proposed approach bypasses theproblem of Cover Source Mismatch -when the
embedding algorithm and bit rate are known-, since it removes the need of a
training database when we have a large enough testing set.Furthermore, we
provide a generic proof of the proposed framework in the machine
learningcontext. Hence, the results of this paper could be extended to other
classification problemssimilar to steganalysis. | ['David Megías', 'Daniel Lerch-Hostalot'] | 2017-03-02 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.19540584e-01 3.08134794e-01 -3.56337219e-01 5.76463044e-02
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3a819b5b-080c-43a4-8459-5f1f82fa59d2 | diverse-image-to-image-translation-via | 1808.00948 | null | http://arxiv.org/abs/1808.00948v1 | http://arxiv.org/pdf/1808.00948v1.pdf | Diverse Image-to-Image Translation via Disentangled Representations | Image-to-image translation aims to learn the mapping between two visual
domains. There are two main challenges for many applications: 1) the lack of
aligned training pairs and 2) multiple possible outputs from a single input
image. In this work, we present an approach based on disentangled
representation for producing diverse outputs without paired training images. To
achieve diversity, we propose to embed images onto two spaces: a
domain-invariant content space capturing shared information across domains and
a domain-specific attribute space. Our model takes the encoded content features
extracted from a given input and the attribute vectors sampled from the
attribute space to produce diverse outputs at test time. To handle unpaired
training data, we introduce a novel cross-cycle consistency loss based on
disentangled representations. Qualitative results show that our model can
generate diverse and realistic images on a wide range of tasks without paired
training data. For quantitative comparisons, we measure realism with user study
and diversity with a perceptual distance metric. We apply the proposed model to
domain adaptation and show competitive performance when compared to the
state-of-the-art on the MNIST-M and the LineMod datasets. | ['Ming-Hsuan Yang', 'Jia-Bin Huang', 'Hung-Yu Tseng', 'Hsin-Ying Lee', 'Maneesh Kumar Singh'] | 2018-08-02 | diverse-image-to-image-translation-via-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hsin-Ying_Lee_Diverse_Image-to-Image_Translation_ECCV_2018_paper.pdf | eccv-2018-9 | ['multimodal-unsupervised-image-to-image', 'synthetic-to-real-translation'] | ['computer-vision', 'computer-vision'] | [ 6.47212327e-01 -1.43807992e-01 -1.13296643e-01 -4.37226802e-01
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5dab8395-bfe9-4a1f-8baf-894a66f52693 | using-ballistocardiography-for-sleep-stage | 2202.01038 | null | https://arxiv.org/abs/2202.01038v2 | https://arxiv.org/pdf/2202.01038v2.pdf | Using Ballistocardiography for Sleep Stage Classification | A practical way of detecting sleep stages has become more necessary as we begin to learn about the vast effects that sleep has on people's lives. The current methods of sleep stage detection are expensive, invasive to a person's sleep, and not practical in a modern home setting. While the method of detecting sleep stages via the monitoring of brain activity, muscle activity, and eye movement, through electroencephalogram in a lab setting, provide the gold standard for detection, this paper aims to investigate a new method that will allow a person to gain similar insight and results with no obtrusion to their normal sleeping habits. Ballistocardiography (BCG) is a non-invasive sensing technology that collects information by measuring the ballistic forces generated by the heart. Using features extracted from BCG such as time of usage, heart rate, respiration rate, relative stroke volume, and heart rate variability, we propose to implement a sleep stage detection algorithm and compare it against sleep stages extracted from a Fitbit Sense Smart Watch. The accessibility, ease of use, and relatively-low cost of the BCG offers many applications and advantages for using this device. By standardizing this device, people will be able to benefit from the BCG in analyzing their own sleep patterns and draw conclusions on their sleep efficiency. This work demonstrates the feasibility of using BCG for an accurate and non-invasive sleep monitoring method that can be set up in the comfort of a one's personal sleep environment. | ['Jiebei Liu', 'Mehdi Boukhechba', 'Krista Nelson', 'Peter Morris'] | 2022-02-02 | null | null | null | null | ['sleep-stage-detection', 'heart-rate-variability'] | ['medical', 'medical'] | [ 1.47549212e-01 -2.82877594e-01 -6.79321364e-02 -3.25100869e-01
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f336e740-ad85-4049-ac25-02554e20b164 | neuricam-video-super-resolution-and | 2207.12496 | null | https://arxiv.org/abs/2207.12496v2 | https://arxiv.org/pdf/2207.12496v2.pdf | NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras | We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low-power (1.1 mW) but only outputs grey-scale, low resolution, and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods. Open-source code: https://github.com/vb000/NeuriCam. | ['Collin Pernu', 'Shyamnath Gollakota', 'Michael Taylor', 'Joshua Smith', 'Ali Saffari', 'Bandhav Veluri'] | 2022-07-25 | null | null | null | null | ['colorization', 'video-super-resolution', 'key-frame-based-video-super-resolution-k-15', 'total-energy'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.47430584e-01 -2.54856735e-01 -4.74535450e-02 5.74492365e-02
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f988a0e9-d021-49d4-b63a-60b3f070c1fb | masked-autoencoders-for-point-cloud-self | 2203.06604 | null | https://arxiv.org/abs/2203.06604v2 | https://arxiv.org/pdf/2203.06604v2.pdf | Masked Autoencoders for Point Cloud Self-supervised Learning | As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud's properties, including leakage of location information and uneven information density. Concretely, we divide the input point cloud into irregular point patches and randomly mask them at a high ratio. Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches. Extensive experiments show that our approach is efficient during pre-training and generalizes well on various downstream tasks. Specifically, our pre-trained models achieve 85.18% accuracy on ScanObjectNN and 94.04% accuracy on ModelNet40, outperforming all the other self-supervised learning methods. We show with our scheme, a simple architecture entirely based on standard Transformers can surpass dedicated Transformer models from supervised learning. Our approach also advances state-of-the-art accuracies by 1.5%-2.3% in the few-shot object classification. Furthermore, our work inspires the feasibility of applying unified architectures from languages and images to the point cloud. | ['Li Yuan', 'Yonghong Tian', 'Wei Liu', 'Francis E. H. Tay', 'Wenxiao Wang', 'Yatian Pang'] | 2022-03-13 | null | null | null | null | ['3d-part-segmentation', 'few-shot-3d-point-cloud-classification', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.24084030e-02 3.43763649e-01 -1.13282613e-01 -2.88311094e-01
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d9259ac7-8fde-4e06-be9d-4575774c410b | enhancing-social-network-hate-detection-using | null | null | https://www.sciencedirect.com/science/article/pii/S1566253523002038 | https://www.sciencedirect.com/science/article/pii/S1566253523002038/pdfft?md5=088fd26f0b8960763d5c7de8b3958527&pid=1-s2.0-S1566253523002038-main.pdf | Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time | Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech. | ['Lior Rokach', 'Shvat Messica', 'Ofir Arbili', 'Or Katz', 'Dan Presil', 'Seffi Cohen'] | 2023-06-17 | null | null | null | information-fusion-2023-6 | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.72334164e-02 -2.59967417e-01 -4.68146205e-02 -2.30138265e-02
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ac07064c-1c0e-468d-ae5d-1af9d55bd7a0 | deep-multi-survey-classification-of-variable | 1810.09440 | null | http://arxiv.org/abs/1810.09440v1 | http://arxiv.org/pdf/1810.09440v1.pdf | Deep multi-survey classification of variable stars | During the last decade, a considerable amount of effort has been made to
classify variable stars using different machine learning techniques. Typically,
light curves are represented as vectors of statistical descriptors or features
that are used to train various algorithms. These features demand big
computational powers that can last from hours to days, making impossible to
create scalable and efficient ways of automatically classifying variable stars.
Also, light curves from different surveys cannot be integrated and analyzed
together when using features, because of observational differences. For
example, having variations in cadence and filters, feature distributions become
biased and require expensive data-calibration models. The vast amount of data
that will be generated soon make necessary to develop scalable machine learning
architectures without expensive integration techniques. Convolutional Neural
Networks have shown impressing results in raw image classification and
representation within the machine learning literature. In this work, we present
a novel Deep Learning model for light curve classification, mainly based on
convolutional units. Our architecture receives as input the differences between
time and magnitude of light curves. It captures the essential classification
patterns regardless of cadence and filter. In addition, we introduce a novel
data augmentation schema for unevenly sampled time series. We test our method
using three different surveys: OGLE-III; Corot; and VVV, which differ in
filters, cadence, and area of the sky. We show that besides the benefit of
scalability, our model obtains state of the art levels accuracy in light curve
classification benchmarks. | ['Karim Pichara', 'Carlos Aguirre', 'Ignacio Becker'] | 2018-10-21 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-3.51951629e-01 -6.82049274e-01 -5.90997841e-03 -6.19950533e-01
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fd7c71c9-0c11-44fc-b442-2bbaa59f010c | chatface-chat-guided-real-face-editing-via | 2305.14742 | null | https://arxiv.org/abs/2305.14742v2 | https://arxiv.org/pdf/2305.14742v2.pdf | ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation | Editing real facial images is a crucial task in computer vision with significant demand in various real-world applications. While GAN-based methods have showed potential in manipulating images especially when combined with CLIP, these methods are limited in their ability to reconstruct real images due to challenging GAN inversion capability. Despite the successful image reconstruction achieved by diffusion-based methods, there are still challenges in effectively manipulating fine-gained facial attributes with textual instructions.To address these issues and facilitate convenient manipulation of real facial images, we propose a novel approach that conduct text-driven image editing in the semantic latent space of diffusion model. By aligning the temporal feature of the diffusion model with the semantic condition at generative process, we introduce a stable manipulation strategy, which perform precise zero-shot manipulation effectively. Furthermore, we develop an interactive system named ChatFace, which combines the zero-shot reasoning ability of large language models to perform efficient manipulations in diffusion semantic latent space. This system enables users to perform complex multi-attribute manipulations through dialogue, opening up new possibilities for interactive image editing. Extensive experiments confirmed that our approach outperforms previous methods and enables precise editing of real facial images, making it a promising candidate for real-world applications. Project page: https://dongxuyue.github.io/chatface/ | ['Li Yuan', 'Yuesheng Zhu', 'Jiaxi Cui', 'Munan Ning', 'Qin Guo', 'Dongxu Yue'] | 2023-05-24 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.93697828e-01 1.06352866e-01 1.22872807e-01 -4.04903084e-01
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db360c41-570d-4056-bd8a-7bbf03430c7d | seeing-wake-words-audio-visual-keyword | 2009.01225 | null | https://arxiv.org/abs/2009.01225v1 | https://arxiv.org/pdf/2009.01225v1.pdf | Seeing wake words: Audio-visual Keyword Spotting | The goal of this work is to automatically determine whether and when a word of interest is spoken by a talking face, with or without the audio. We propose a zero-shot method suitable for in the wild videos. Our key contributions are: (1) a novel convolutional architecture, KWS-Net, that uses a similarity map intermediate representation to separate the task into (i) sequence matching, and (ii) pattern detection, to decide whether the word is there and when; (2) we demonstrate that if audio is available, visual keyword spotting improves the performance both for a clean and noisy audio signal. Finally, (3) we show that our method generalises to other languages, specifically French and German, and achieves a comparable performance to English with less language specific data, by fine-tuning the network pre-trained on English. The method exceeds the performance of the previous state-of-the-art visual keyword spotting architecture when trained and tested on the same benchmark, and also that of a state-of-the-art lip reading method. | ['Triantafyllos Afouras', 'Andrew Zisserman', 'Themos Stafylakis', 'Samuel Albanie', 'Liliane Momeni'] | 2020-09-02 | null | null | null | null | ['visual-keyword-spotting'] | ['computer-vision'] | [ 4.97400045e-01 -8.25353190e-02 -1.07530773e-01 -1.64540574e-01
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10c13a85-e02e-4172-97bd-6aa292c208fe | trajectory-space-factorization-for-deep-video | 1908.08289 | null | https://arxiv.org/abs/1908.08289v1 | https://arxiv.org/pdf/1908.08289v1.pdf | Trajectory Space Factorization for Deep Video-Based 3D Human Pose Estimation | Existing deep learning approaches on 3d human pose estimation for videos are either based on Recurrent or Convolutional Neural Networks (RNNs or CNNs). However, RNN-based frameworks can only tackle sequences with limited frames because sequential models are sensitive to bad frames and tend to drift over long sequences. Although existing CNN-based temporal frameworks attempt to address the sensitivity and drift problems by concurrently processing all input frames in the sequence, the existing state-of-the-art CNN-based framework is limited to 3d pose estimation of a single frame from a sequential input. In this paper, we propose a deep learning-based framework that utilizes matrix factorization for sequential 3d human poses estimation. Our approach processes all input frames concurrently to avoid the sensitivity and drift problems, and yet outputs the 3d pose estimates for every frame in the input sequence. More specifically, the 3d poses in all frames are represented as a motion matrix factorized into a trajectory bases matrix and a trajectory coefficient matrix. The trajectory bases matrix is precomputed from matrix factorization approaches such as Singular Value Decomposition (SVD) or Discrete Cosine Transform (DCT), and the problem of sequential 3d pose estimation is reduced to training a deep network to regress the trajectory coefficient matrix. We demonstrate the effectiveness of our framework on long sequences by achieving state-of-the-art performances on multiple benchmark datasets. Our source code is available at: https://github.com/jiahaoLjh/trajectory-pose-3d. | ['Jiahao Lin', 'Gim Hee Lee'] | 2019-08-22 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.42612481e-01 -5.06006420e-01 -2.72560924e-01 -8.37760195e-02
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50759bfa-f6c7-483a-a720-312c9a4ba76e | june-germany-an-agent-based-epidemiology | 2303.05742 | null | https://arxiv.org/abs/2303.05742v1 | https://arxiv.org/pdf/2303.05742v1.pdf | JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple Virus Strains, Vaccinations and Testing Campaigns | The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper, we present a new version of the framework specifically adapted for Germany, which allows the simulation of the entire German population using publicly available information on households, schools, universities, workplaces, and mobility data for Germany. Moreover, JuneGermany incorporates testing and vaccination strategies within the population as well as the simultaneous handling of several different virus strains. First validation tests of the framework have been performed for the state of Rhineland Palatinate based on data collected between October 2020 and December 2020 and then extrapolated to March 2021, i.e. the end of the second wave. | ['Matthias Schott', 'Friedemann Neuhaus', 'Andrew Iskauskas', 'Lucas Heger', 'Kerem Akdogan'] | 2023-03-10 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.87478942e-01 1.09199680e-01 5.88340051e-02 7.48068243e-02
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fa0f32ff-b434-4e4b-9d59-eebed2cf4bbb | a-dynamic-programming-algorithm-for-tree | null | null | https://aclanthology.info/papers/N15-1049/n15-1049 | https://www.aclweb.org/anthology/N15-1049 | A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization | null | ['Shin-ichi Minato', 'Tsutomu Hirao', 'Norihito Yasuda', 'Masaaki Nagata', 'Masaaki Nishino'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
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65e17575-3d78-4325-b087-391e6d9b2dd2 | weakly-supervised-action-selection-learning | 2105.02439 | null | https://arxiv.org/abs/2105.02439v1 | https://arxiv.org/pdf/2105.02439v1.pdf | Weakly Supervised Action Selection Learning in Video | Localizing actions in video is a core task in computer vision. The weakly supervised temporal localization problem investigates whether this task can be adequately solved with only video-level labels, significantly reducing the amount of expensive and error-prone annotation that is required. A common approach is to train a frame-level classifier where frames with the highest class probability are selected to make a video-level prediction. Frame level activations are then used for localization. However, the absence of frame-level annotations cause the classifier to impart class bias on every frame. To address this, we propose the Action Selection Learning (ASL) approach to capture the general concept of action, a property we refer to as "actionness". Under ASL, the model is trained with a novel class-agnostic task to predict which frames will be selected by the classifier. Empirically, we show that ASL outperforms leading baselines on two popular benchmarks THUMOS-14 and ActivityNet-1.2, with 10.3% and 5.7% relative improvement respectively. We further analyze the properties of ASL and demonstrate the importance of actionness. Full code for this work is available here: https://github.com/layer6ai-labs/ASL. | ['Guangwei Yu', 'Maksims Volkovs', 'Satya Krishna Gorti', 'Junwei Ma'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ma_Weakly_Supervised_Action_Selection_Learning_in_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 4.34659421e-01 -1.47804916e-01 -8.23070288e-01 -4.18615967e-01
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e035fce2-3e8a-4c08-8586-5191ad871f4a | guaranteed-quantization-error-computation-for | 2304.13812 | null | https://arxiv.org/abs/2304.13812v1 | https://arxiv.org/pdf/2304.13812v1.pdf | Guaranteed Quantization Error Computation for Neural Network Model Compression | Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforward neural network and its quantized version to produce the exact output difference between two neural networks. Then, optimization-based methods and reachability analysis methods are applied to the merged neural network to compute the guaranteed quantization error. Finally, a numerical example is proposed to validate the applicability and effectiveness of the proposed approach. | ['Weiming Xiang', 'Zihao Mo', 'Wesley Cooke'] | 2023-04-26 | null | null | null | null | ['neural-network-compression', 'model-compression', 'neural-network-compression'] | ['methodology', 'methodology', 'miscellaneous'] | [ 7.82442451e-01 6.65844262e-01 -1.83182850e-01 -1.62575006e-01
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92e7e175-c752-46ba-8b7c-91d7e1634d0c | does-the-geometry-of-word-embeddings-help | null | null | https://aclanthology.org/W17-2628 | https://aclanthology.org/W17-2628.pdf | Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations | We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like tf-idf, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen datasets. | ['Ravich', 'Paul Michel', 'Abhilasha er', 'Shruti Rijhwani'] | 2017-08-01 | null | null | null | ws-2017-8 | ['document-embedding'] | ['methodology'] | [-2.92678863e-01 -1.19342752e-01 -5.30850217e-02 -4.20615375e-01
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e27846ff-1167-4b08-98f1-7a04faa03121 | psvt-end-to-end-multi-person-3d-pose-and | 2303.09187 | null | https://arxiv.org/abs/2303.09187v1 | https://arxiv.org/pdf/2303.09187v1.pdf | PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers | Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In this paper, we propose a new end-to-end multi-person 3D Pose and Shape estimation framework with progressive Video Transformer, termed PSVT. In PSVT, a spatio-temporal encoder (STE) captures the global feature dependencies among spatial objects. Then, spatio-temporal pose decoder (STPD) and shape decoder (STSD) capture the global dependencies between pose queries and feature tokens, shape queries and feature tokens, respectively. To handle the variances of objects as time proceeds, a novel scheme of progressive decoding is used to update pose and shape queries at each frame. Besides, we propose a novel pose-guided attention (PGA) for shape decoder to better predict shape parameters. The two components strengthen the decoder of PSVT to improve performance. Extensive experiments on the four datasets show that PSVT achieves stage-of-the-art results. | ['Jingdong Wang', 'Dongmei Fu', 'Chang Xu', 'Errui Ding', 'Junyu Han', 'Haocheng Feng', 'Jian Wang', 'Yang Qiansheng', 'Zhongwei Qiu'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_PSVT_End-to-End_Multi-Person_3D_Pose_and_Shape_Estimation_With_Progressive_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-4.63873707e-02 -3.92907888e-01 8.86403918e-02 -4.97469991e-01
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1a91d85b-438f-49fd-88e0-74296a06e7b9 | zero-shot-video-object-segmentation-via-1 | 2001.06807 | null | https://arxiv.org/abs/2001.06807v1 | https://arxiv.org/pdf/2001.06807v1.pdf | Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks | This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pairs as edges. The underlying pair-wise relations are described by a differentiable attention mechanism. Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation. Experimental results on three video segmentation datasets show that AGNN sets a new state-of-the-art in each case. To further demonstrate the generalizability of our framework, we extend AGNN to an additional task: image object co-segmentation (IOCS). We perform experiments on two famous IOCS datasets and observe again the superiority of our AGNN model. The extensive experiments verify that AGNN is able to learn the underlying semantic/appearance relationships among video frames or related images, and discover the common objects. | ['Xiankai Lu', 'David Crandall', 'Jianbing Shen', 'Wenguan Wang', 'Ling Shao'] | 2020-01-19 | zero-shot-video-object-segmentation-via | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Zero-Shot_Video_Object_Segmentation_via_Attentive_Graph_Neural_Networks_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Zero-Shot_Video_Object_Segmentation_via_Attentive_Graph_Neural_Networks_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.33038738e-01 2.25832015e-01 -3.86699528e-01 -2.85180420e-01
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337ab378-cc5e-4b0a-b343-dbce5ee1ddb5 | crrn-multi-scale-guided-concurrent-reflection | 1805.11802 | null | http://arxiv.org/abs/1805.11802v1 | http://arxiv.org/pdf/1805.11802v1.pdf | CRRN: Multi-Scale Guided Concurrent Reflection Removal Network | Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description capability to the properties of real-world reflections. In this
paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle
this problem in a unified framework. Our proposed network integrates image
appearance information and multi-scale gradient information with human
perception inspired loss function, and is trained on a new dataset with 3250
reflection images taken under diverse real-world scenes. Extensive experiments
on a public benchmark dataset show that the proposed method performs favorably
against state-of-the-art methods. | ['Ah-Hwee Tan', 'Ling-Yu Duan', 'Alex C. Kot', 'Renjie Wan', 'Boxin Shi'] | 2018-05-30 | crrn-multi-scale-guided-concurrent-reflection-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.pdf | cvpr-2018-6 | ['reflection-removal'] | ['computer-vision'] | [ 4.89475280e-01 -5.07232666e-01 3.48642945e-01 -4.18145567e-01
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3f2d9e98-1ca7-46d8-9cc6-3da9f1348205 | learning-discriminative-shrinkage-deep | 2111.13876 | null | https://arxiv.org/abs/2111.13876v3 | https://arxiv.org/pdf/2111.13876v3.pdf | Learning Discriminative Shrinkage Deep Networks for Image Deconvolution | Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging and usually leads to complex optimization problems which are difficult to solve. In this paper, we propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. In contrast to most existing methods that use deep convolutional neural networks (CNNs) or radial basis functions to simply learn the regularization term, we formulate both the data term and regularization term and split the deconvolution model into data-related and regularization-related sub-problems according to the alternating direction method of multipliers. We explore the properties of the Maxout function and develop a deep CNN model with a Maxout layer to learn discriminative shrinkage functions to directly approximate the solutions of these two sub-problems. Moreover, given the fast-Fourier-transform-based image restoration usually leads to ringing artifacts while conjugate-gradient-based approach is time-consuming, we develop the Conjugate Gradient Network to restore the latent clear images effectively and efficiently. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy. | ['Ming-Hsuan Yang', 'Shao-Yi Chien', 'Jinshan Pan', 'Pin-Hung Kuo'] | 2021-11-27 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 8.07560757e-02 -2.39363998e-01 2.24773526e-01 -5.48469007e-01
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0a464271-7151-42f4-87ff-e59cb77d18cf | making-a-bird-ai-expert-work-for-you-and-me | 2112.02747 | null | https://arxiv.org/abs/2112.02747v1 | https://arxiv.org/pdf/2112.02747v1.pdf | Making a Bird AI Expert Work for You and Me | As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however commonly accepted in the literature, underlines a fundamental question interfacing AI and human -- what constitutes transferable knowledge for human to learn from AI? This paper sets out to answer this very question using FGVC as a test bed. Specifically, we envisage a scenario where a trained FGVC model (the AI expert) functions as a knowledge provider in enabling average people (you and me) to become better domain experts ourselves, i.e. those capable in distinguishing between "Whip-poor-will" and "Mallard". Fig. 1 lays out our approach in answering this question. Assuming an AI expert trained using expert human labels, we ask (i) what is the best transferable knowledge we can extract from AI, and (ii) what is the most practical means to measure the gains in expertise given that knowledge? On the former, we propose to represent knowledge as highly discriminative visual regions that are expert-exclusive. For that, we devise a multi-stage learning framework, which starts with modelling visual attention of domain experts and novices before discriminatively distilling their differences to acquire the expert exclusive knowledge. For the latter, we simulate the evaluation process as book guide to best accommodate the learning practice of what is accustomed to humans. A comprehensive human study of 15,000 trials shows our method is able to consistently improve people of divergent bird expertise to recognise once unrecognisable birds. Interestingly, our approach also leads to improved conventional FGVC performance when the extracted knowledge defined is utilised as means to achieve discriminative localisation. Codes are available at: https://github.com/PRIS-CV/Making-a-Bird-AI-Expert-Work-for-You-and-Me | ['Jun Guo', 'Yi-Zhe Song', 'Zhanyu Ma', 'Ruoyi Du', 'Kaiyue Pang', 'Dongliang Chang'] | 2021-12-06 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.49392372e-02 5.49031720e-02 3.04211497e-01 -2.46095866e-01
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e70d05e6-6964-4f62-8a85-8131ef898591 | error-bounded-foreground-and-background | 1908.09539 | null | https://arxiv.org/abs/1908.09539v1 | https://arxiv.org/pdf/1908.09539v1.pdf | Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos | Detecting moving objects from ground-based videos is commonly achieved by using background subtraction techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve background subtraction in the developed method of Low-rank and Structured Sparse Decomposition (LSD). However, when this method is applied to satellite videos where spatial resolution is poor and targets' contrast to the background is low, its performance is limited as the data no longer fits adequately either the foreground structure or the background model. In this paper, we handle these unexplained data explicitly and address the moving target detection from space as one of the pioneer studies. We propose a technique by extending the decomposition formulation with bounded errors, named Extended Low-rank and Structured Sparse Decomposition (E-LSD). This formulation integrates low-rank background, structured sparse foreground and their residuals in a matrix decomposition problem. We provide an effective solution by introducing an alternative treatment and adopting the direct extension of Alternating Direction Method of Multipliers (ADMM). The proposed E-LSD was validated on two satellite videos, and experimental results demonstrate the improvement in background modeling with boosted moving object detection precision over state-of-the-art methods. | ['Junpeng Zhang', 'Xiuping Jia', 'Jiankun Hu'] | 2019-08-26 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 6.46423101e-01 -4.94683743e-01 1.06329136e-01 5.81772206e-03
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88f30e06-c805-486d-8688-85214d3a5af4 | the-power-of-log-sum-exp-sequential-density | 2105.13636 | null | https://arxiv.org/abs/2105.13636v2 | https://arxiv.org/pdf/2105.13636v2.pdf | The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization | We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is known to be asymptotically optimal for this setting, but contains a critical assumption that hinders broad real-world applications; the MSPRT requires the underlying probability density. To address this problem, we propose to solve density ratio matrix estimation (DRME), a novel type of density ratio estimation that consists of estimating matrices of multiple density ratios with constraints and thus is more challenging than the conventional density ratio estimation. We propose a log-sum-exp-type loss function (LSEL) for solving DRME and prove the following: (i) the LSEL provides the true density ratio matrix as the sample size of the training set increases (consistency); (ii) it assigns larger gradients to harder classes (hard class weighting effect); and (iii) it provides discriminative scores even on class-imbalanced datasets (guess-aversion). Our overall architecture for early classification, MSPRT-TANDEM, statistically significantly outperforms baseline models on four datasets including action recognition, especially in the early stage of sequential observations. Our code and datasets are publicly available at: https://github.com/TaikiMiyagawa/MSPRT-TANDEM. | ['Akinori F. Ebihara', 'Taiki Miyagawa'] | 2021-05-28 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 9.58533734e-02 -3.18621874e-01 -5.75251758e-01 -4.40450042e-01
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f3b7e997-0e18-4e6d-9f02-20fdf7c80a28 | evaluating-resilience-of-encrypted-traffic | 2105.14564 | null | https://arxiv.org/abs/2105.14564v1 | https://arxiv.org/pdf/2105.14564v1.pdf | Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks | Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic. Classification of encrypted traffic can become more challenging in the presence of adversarial attacks that target the learning algorithms. In this paper, we focus on investigating the effectiveness of different evasion attacks and see how resilient machine and deep learning algorithms are. Namely, we test C4.5 Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). In most of our experimental results, deep learning shows better resilience against the adversarial samples in comparison to machine learning. Whereas, the impact of the attack varies depending on the type of attack. | ['Ashraf Matrawy', 'Danish Sattar', 'Ramy Maarouf'] | 2021-05-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.56378195e-01 -3.40096921e-01 -1.47196770e-01 -7.74798542e-02
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b58c6db9-cb01-4f3a-a790-fa16cbdc934a | skin-lesion-classification-using-deep-neural | 1911.07817 | null | https://arxiv.org/abs/1911.07817v1 | https://arxiv.org/pdf/1911.07817v1.pdf | Skin Lesion Classification Using Deep Neural Network | This paper reports the methods and techniques we have developed for classify dermoscopic images (task 1) of the ISIC 2019 challenge dataset for skin lesion classification, our approach aims to use ensemble deep neural network with some powerful techniques to deal with unbalance data sets as its the main problem for this challenge in a move to increase the performance of CNNs model. | ['Alla Eddine Guissous'] | 2019-11-18 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.11729282e-01 3.98153327e-02 -2.06373706e-01 -1.29832685e-01
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13940183-644b-49ac-850a-d704d1b0216b | sign-language-recognition-system-using | 2201.01486 | null | https://arxiv.org/abs/2201.01486v2 | https://arxiv.org/pdf/2201.01486v2.pdf | Sign Language Recognition System using TensorFlow Object Detection API | Communication is defined as the act of sharing or exchanging information, ideas or feelings. To establish communication between two people, both of them are required to have knowledge and understanding of a common language. But in the case of deaf and dumb people, the means of communication are different. Deaf is the inability to hear and dumb is the inability to speak. They communicate using sign language among themselves and with normal people but normal people do not take seriously the importance of sign language. Not everyone possesses the knowledge and understanding of sign language which makes communication difficult between a normal person and a deaf and dumb person. To overcome this barrier, one can build a model based on machine learning. A model can be trained to recognize different gestures of sign language and translate them into English. This will help a lot of people in communicating and conversing with deaf and dumb people. The existing Indian Sing Language Recognition systems are designed using machine learning algorithms with single and double-handed gestures but they are not real-time. In this paper, we propose a method to create an Indian Sign Language dataset using a webcam and then using transfer learning, train a TensorFlow model to create a real-time Sign Language Recognition system. The system achieves a good level of accuracy even with a limited size dataset. | ['Sudhakar Singh', 'Richa Mishra', 'Amisha Gangwar', 'Sharvani Srivastava'] | 2022-01-05 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.70546407e-01 -3.92706841e-01 1.02619408e-02 -6.32467568e-01
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d3f9f020-4212-4e7a-84b9-66975579337c | semi-targeted-model-poisoning-attack-on | 2203.11633 | null | https://arxiv.org/abs/2203.11633v2 | https://arxiv.org/pdf/2203.11633v2.pdf | Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis | Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such compromised models are tampered with to perform adversary-desired behaviors. In particular, we considered a semi-targeted situation where the source class is predetermined however the target class is not. The goal is to cause the global classifier to misclassify data of the source class. Though approaches such as label flipping have been adopted to inject poisoned parameters into FL, it has been shown that their performances are usually class-sensitive varying with different target classes applied. Typically, an attack can become less effective when shifting to a different target class. To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) to enhance a poisoning attack by finding the optimized target class in the feature space. Moreover, we studied a more challenging situation where an adversary had limited prior knowledge about a client's data. To tackle this problem, ADA deduces pair-wise distances between different classes in the latent feature space from shared model parameters based on the backward error analysis. We performed extensive empirical evaluations on ADA by varying the factor of attacking frequency in three different image classification tasks. As a result, ADA succeeded in increasing the attack performance by 1.8 times in the most challenging case with an attacking frequency of 0.01. | ['Jun Sakuma', 'Hideya Ochiai', 'Yuwei Sun'] | 2022-03-22 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 3.61114651e-01 -1.16735034e-01 7.34662712e-02 -2.99837254e-02
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b6fdf66e-4bfb-4d8c-b7a3-5024c8daa224 | domain-class-correlation-decomposition-for | 2106.15206 | null | https://arxiv.org/abs/2106.15206v1 | https://arxiv.org/pdf/2106.15206v1.pdf | Domain-Class Correlation Decomposition for Generalizable Person Re-Identification | Domain generalization in person re-identification is a highly important meaningful and practical task in which a model trained with data from several source domains is expected to generalize well to unseen target domains. Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training. However, in person re-identification, the domain and class are correlated, and we theoretically show that domain adversarial learning will lose certain information about class due to this domain-class correlation. Inspired by casual inference, we propose to perform interventions to the domain factor $d$, aiming to decompose the domain-class correlation. To achieve this goal, we proposed estimating the resulting representation $z^{*}$ caused by the intervention through first- and second-order statistical characteristic matching. Specifically, we build a memory bank to restore the statistical characteristics of each domain. Then, we use the newly generated samples $\{z^{*},y,d^{*}\}$ to compute the loss function. These samples are domain-class correlation decomposed; thus, we can learn a domain-invariant representation that can capture more class-related features. Extensive experiments show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark. | ['Xinmei Tian', 'Kaiwen Yang'] | 2021-06-29 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 3.45955104e-01 -1.00247532e-01 -9.21095163e-02 -4.01068419e-01
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2389ebce-f48d-4de4-838e-67c7d74c6478 | look-read-and-enrich-learning-from-scientific | 1909.09070 | null | https://arxiv.org/abs/1909.09070v1 | https://arxiv.org/pdf/1909.09070v1.pdf | Look, Read and Enrich. Learning from Scientific Figures and their Captions | Compared to natural images, understanding scientific figures is particularly hard for machines. However, there is a valuable source of information in scientific literature that until now has remained untapped: the correspondence between a figure and its caption. In this paper we investigate what can be learnt by looking at a large number of figures and reading their captions, and introduce a figure-caption correspondence learning task that makes use of our observations. Training visual and language networks without supervision other than pairs of unconstrained figures and captions is shown to successfully solve this task. We also show that transferring lexical and semantic knowledge from a knowledge graph significantly enriches the resulting features. Finally, we demonstrate the positive impact of such features in other tasks involving scientific text and figures, like multi-modal classification and machine comprehension for question answering, outperforming supervised baselines and ad-hoc approaches. | ['Jose Manuel Gomez-Perez', 'Raul Ortega'] | 2019-09-19 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 2.94756025e-01 4.35051054e-01 6.42484650e-02 -4.57134753e-01
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d1c3ca10-5a3b-45db-9e11-eb04adb57495 | multi-modal-domain-adaptation-for-fine | 2001.09691 | null | https://arxiv.org/abs/2001.09691v2 | https://arxiv.org/pdf/2001.09691v2.pdf | Multi-Modal Domain Adaptation for Fine-Grained Action Recognition | Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. However, these approaches have not explored the multi-modal nature of video within each domain. In this work we exploit the correspondence of modalities as a self-supervised alignment approach for UDA in addition to adversarial alignment. We test our approach on three kitchens from our large-scale dataset, EPIC-Kitchens, using two modalities commonly employed for action recognition: RGB and Optical Flow. We show that multi-modal self-supervision alone improves the performance over source-only training by 2.4% on average. We then combine adversarial training with multi-modal self-supervision, showing that our approach outperforms other UDA methods by 3%. | ['Jonathan Munro', 'Dima Damen'] | 2020-01-27 | multi-modal-domain-adaptation-for-fine-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Munro_Multi-Modal_Domain_Adaptation_for_Fine-Grained_Action_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Munro_Multi-Modal_Domain_Adaptation_for_Fine-Grained_Action_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 6.62362695e-01 -1.47328541e-01 -1.49686471e-01 -2.03294054e-01
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5c7596e6-ba15-4115-a5fc-a7630ae6afa8 | inductive-entity-representations-from-text | 2010.03496 | null | https://arxiv.org/abs/2010.03496v3 | https://arxiv.org/pdf/2010.03496v3.pdf | Inductive Entity Representations from Text via Link Prediction | Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. Recent work has begun to explore the use of textual descriptions available in knowledge graphs to learn vector representations of entities in order to preform link prediction. However, the extent to which these representations learned for link prediction generalize to other tasks is unclear. This is important given the cost of learning such representations. Ideally, we would prefer representations that do not need to be trained again when transferring to a different task, while retaining reasonable performance. In this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. We consider the inductive link prediction and entity classification tasks, which involve entities not seen during training. We also consider an information retrieval task for entity-oriented search. We evaluate an architecture based on a pretrained language model, that exhibits strong generalization to entities not observed during training, and outperforms related state-of-the-art methods (22% MRR improvement in link prediction on average). We further provide evidence that the learned representations transfer well to other tasks without fine-tuning. In the entity classification task we obtain an average improvement of 16% in accuracy compared with baselines that also employ pre-trained models. In the information retrieval task, we obtain significant improvements of up to 8.8% in NDCG@10 for natural language queries. We thus show that the learned representations are not limited KG-specific tasks, and have greater generalization properties than evaluated in previous work. | ['Paul Groth', 'Michael Cochez', 'Daniel Daza'] | 2020-10-07 | null | null | null | null | ['inductive-link-prediction', 'inductive-knowledge-graph-completion'] | ['graphs', 'knowledge-base'] | [ 3.46674882e-02 5.88525832e-01 -6.46522284e-01 -1.56541064e-01
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80a5a049-f8df-484b-9540-bd67a36e616c | building-efficient-cnn-architecture-for | 1804.01259 | null | http://arxiv.org/abs/1804.01259v2 | http://arxiv.org/pdf/1804.01259v2.pdf | Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition | Deep convolutional networks based methods have brought great breakthrough in
images classification, which provides an end-to-end solution for handwritten
Chinese character recognition(HCCR) problem through learning discriminative
features automatically. Nevertheless, state-of-the-art CNNs appear to incur
huge computation cost, and require the storage of a large number of parameters
especially in fully connected layers, which is difficult to deploy such
networks into alternative hardware device with the limit of computation amount.
To solve the storage problem, we propose a novel technique called Global
Weighted Arverage Pooling for reducing the parameters in fully connected layer
without loss in accuracy. Besides, we implement a cascaded model in single CNN
by adding mid output layer to complete recognition as early as possible, which
reduces average inference time significantly. Experiments were performed on the
ICDAR-2013 offline HCCR dataset, and it is found that the proposed approach
only needs 6.9ms for classfying a chracter image on average, and achieves the
state-of-the-art accuracy of 97.1% while requiring only 3.3MB for storage. | ['Nanjun Teng', 'Zhiyuan Li', 'Min Jin', 'Huaxiang Lu'] | 2018-04-04 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 3.98002230e-02 -4.62193668e-01 5.06905951e-02 -6.25355899e-01
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085eb006-b062-48b0-ac62-a2a1617ad22d | lite-hdseg-lidar-semantic-segmentation-using | 2103.08852 | null | https://arxiv.org/abs/2103.08852v1 | https://arxiv.org/pdf/2103.08852v1.pdf | Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions | Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings. Scene understanding is one of the crucial components of perception modules. Among all available sensors, LiDARs are one of the essential sensing modalities of autonomous driving systems due to their active sensing nature with high resolution of sensor readings. Accurate and fast semantic segmentation methods are needed to fully utilize LiDAR sensors for scene understanding. In this paper, we present Lite-HDSeg, a novel real-time convolutional neural network for semantic segmentation of full $3$D LiDAR point clouds. Lite-HDSeg can achieve the best accuracy vs. computational complexity trade-off in SemanticKitti benchmark and is designed on the basis of a new encoder-decoder architecture with light-weight harmonic dense convolutions as its core. Moreover, we introduce ICM, an improved global contextual module to capture multi-scale contextual features, and MCSPN, a multi-class Spatial Propagation Network to further refine the semantic boundaries. Our experimental results show that the proposed method outperforms state-of-the-art semantic segmentation approaches which can run real-time, thus is suitable for robotic and autonomous driving applications. | ['Liu Bingbing', 'Ehsan Taghavi', 'Ran Cheng', 'Ryan Razani'] | 2021-03-16 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.76052713e-01 -6.49370477e-02 -4.85709496e-02 -9.43777978e-01
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37d839b9-9f5e-4c11-847f-149e039621d7 | language-models-are-few-shot-learners | 2005.14165 | null | https://arxiv.org/abs/2005.14165v4 | https://arxiv.org/pdf/2005.14165v4.pdf | Language Models are Few-Shot Learners | Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general. | ['Scott Gray', 'Christopher Hesse', 'Rewon Child', 'Gretchen Krueger', 'Ariel Herbert-Voss', 'Arvind Neelakantan', 'Sandhini Agarwal', 'Mark Chen', 'Tom B. Brown', 'Pranav Shyam', 'Nick Ryder', 'Mateusz Litwin', 'Jeffrey Wu', 'Ilya Sutskever', 'Eric Sigler', 'Clemens Winter', 'Benjamin Chess', 'Amanda Askell', 'Alec Radford', 'Melanie Subbiah', 'Jared Kaplan', 'Jack Clark', 'Dario Amodei', 'Tom Henighan', 'Sam McCandlish', 'Prafulla Dhariwal', 'Girish Sastry', 'Daniel M. Ziegler', 'Christopher Berner', 'Benjamin Mann', 'Aditya Ramesh'] | 2020-05-28 | null | http://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf | neurips-2020-12 | ['multi-task-language-understanding', 'unsupervised-machine-translation'] | ['methodology', 'natural-language-processing'] | [ 2.74567068e-01 1.14149459e-01 -7.35276714e-02 -4.51432645e-01
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7fb4623d-d731-4077-ac56-0c1465bfb325 | highlight-specular-reflection-separation | 2207.03543 | null | https://arxiv.org/abs/2207.03543v1 | https://arxiv.org/pdf/2207.03543v1.pdf | Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues | This paper is concerned with specular reflection removal based on tensor low-rank decomposition framework with the help of polarization information. Our method is motivated by the observation that the specular highlight of an image is sparsely distributed while the remaining diffuse reflection can be well approximated by a linear combination of several distinct colors using a low-rank and sparse decomposition framework. Unlike current solutions, our tensor low-rank decomposition keeps the spatial structure of specular and diffuse information which enables us to recover the diffuse image under strong specular reflection or in saturated regions. We further define and impose a new polarization regularization term as constraint on color channels. This regularization boosts the performance of the method to recover an accurate diffuse image by handling the color distortion, a common problem of chromaticity-based methods, especially in case of strong specular reflection. Through comprehensive experiments on both synthetic and real polarization images, we demonstrate that our method is able to significantly improve the accuracy of highlight specular removal, and outperform the competitive methods to recover the diffuse image, especially in regions of strong specular reflection or in saturated areas. | ['Hong Zhang', 'Moein Shakeri'] | 2022-07-07 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 4.65480745e-01 -5.34416199e-01 2.34439105e-01 1.71159893e-01
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06b81ac8-4f51-4335-a61c-01bb91f4cde0 | gradient-guided-unsupervised-text-style | 2202.00469 | null | https://arxiv.org/abs/2202.00469v1 | https://arxiv.org/pdf/2202.00469v1.pdf | Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning | Text style transfer is a challenging text generation problem, which aims at altering the style of a given sentence to a target one while keeping its content unchanged. Since there is a natural scarcity of parallel datasets, recent works mainly focus on solving the problem in an unsupervised manner. However, previous gradient-based works generally suffer from the deficiencies as follows, namely: (1) Content migration. Previous approaches lack explicit modeling of content invariance and are thus susceptible to content shift between the original sentence and the transferred one. (2) Style misclassification. A natural drawback of the gradient-guided approaches is that the inference process is homogeneous with a line of adversarial attack, making latent optimization easily becomes an attack to the classifier due to misclassification. This leads to difficulties in achieving high transfer accuracy. To address the problems, we propose a novel gradient-guided model through a contrastive paradigm for text style transfer, to explicitly gather similar semantic sentences, and to design a siamese-structure based style classifier for alleviating such two issues, respectively. Experiments on two datasets show the effectiveness of our proposed approach, as compared to the state-of-the-arts. | ['Wei Wei', 'Ziao Li', 'Chenghao Fan'] | 2022-01-23 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 7.45282650e-01 -8.32550600e-02 -3.37049440e-02 -3.38775843e-01
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51a85541-af10-42f3-8c46-2b09c130984a | tweet-fid-an-annotated-dataset-for-multiple | 2205.10726 | null | https://arxiv.org/abs/2205.10726v2 | https://arxiv.org/pdf/2205.10726v2.pdf | TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks | Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a dearth of labeled datasets for developing effective outbreak detection models. To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. TWEET-FID collected from Twitter is annotated with three facets: tweet class, entity type, and slot type, with labels produced by experts as well as by crowdsource workers. We introduce several domain tasks leveraging these three facets: text relevance classification (TRC), entity mention detection (EMD), and slot filling (SF). We describe the end-to-end methodology for dataset design, creation, and labeling for supporting model development for these tasks. A comprehensive set of results for these tasks leveraging state-of-the-art single- and multi-task deep learning methods on the TWEET-FID dataset are provided. This dataset opens opportunities for future research in foodborne outbreak detection. | ['Elke Rundensteiner', 'Hao Feng', 'Thomas Hartvigsen', 'Dandan Tao', 'Dongyu Zhang', 'Ruofan Hu'] | 2022-05-22 | null | https://aclanthology.org/2022.lrec-1.668 | https://aclanthology.org/2022.lrec-1.668.pdf | lrec-2022-6 | ['slot-filling'] | ['natural-language-processing'] | [ 2.16301814e-01 3.45577672e-02 -2.85640836e-01 -3.27846438e-01
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b7f44a81-6389-48a3-904d-e50676702cca | era-entity-relationship-aware-video | 2109.02625 | null | https://arxiv.org/abs/2109.02625v1 | https://arxiv.org/pdf/2109.02625v1.pdf | ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN | Video summarization aims to simplify large scale video browsing by generating concise, short summaries that diver from but well represent the original video. Due to the scarcity of video annotations, recent progress for video summarization concentrates on unsupervised methods, among which the GAN based methods are most prevalent. This type of methods includes a summarizer and a discriminator. The summarized video from the summarizer will be assumed as the final output, only if the video reconstructed from this summary cannot be discriminated from the original one by the discriminator. The primary problems of this GAN based methods are two folds. First, the summarized video in this way is a subset of original video with low redundancy and contains high priority events/entities. This summarization criterion is not enough. Second, the training of the GAN framework is not stable. This paper proposes a novel Entity relationship Aware video summarization method (ERA) to address the above problems. To be more specific, we introduce an Adversarial Spatio Temporal network to construct the relationship among entities, which we think should also be given high priority in the summarization. The GAN training problem is solved by introducing the Wasserstein GAN and two newly proposed video patch/score sum losses. In addition, the score sum loss can also relieve the model sensitivity to the varying video lengths, which is an inherent problem for most current video analysis tasks. Our method substantially lifts the performance on the target benchmark datasets and exceeds the current leaderboard Rank 1 state of the art CSNet (2.1% F1 score increase on TVSum and 3.1% F1 score increase on SumMe). We hope our straightforward yet effective approach will shed some light on the future research of unsupervised video summarization. | ['Claudio T. Silva', 'Jianzhe Lin', 'Guande Wu'] | 2021-09-06 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 5.12172639e-01 2.04678267e-01 -2.41692707e-01 -1.55223176e-01
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3079fce5-dbdb-45fd-88b7-d382ae73784c | a-deep-generative-approach-to-native-language | null | null | https://aclanthology.org/2020.coling-main.159 | https://aclanthology.org/2020.coling-main.159.pdf | A Deep Generative Approach to Native Language Identification | Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. | ['Walter Daelemans', 'Ilia Markov', 'Ehsan Lotfi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.01660359e-01 -1.73484117e-01 -2.89216071e-01 -3.00741583e-01
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c93fd1b6-3455-46d1-b49d-0940c8d730b3 | unsupervised-learning-of-the-total-variation | 2206.04406 | null | https://arxiv.org/abs/2206.04406v1 | https://arxiv.org/pdf/2206.04406v1.pdf | Unsupervised Learning of the Total Variation Flow | The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images such as sharp edges and enables spectral, scale, and texture analysis. The standard numerical approach for TV flow requires solving multiple non-smooth optimisation problems. Even with state-of-the-art convex optimisation techniques, this is often prohibitively expensive and strongly motivates the use of alternative, faster approaches. Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, a neural network approach to compute the solution of the TV flow given an initial image and a time instance. We significantly speed up the computation time by more than one order of magnitude and show that the TVflowNET approximates the TV flow solution with high fidelity. This is a preliminary report, more details are to follow. | ['Carola-Bibiane Schönlieb', 'Yury Korolev', 'Sören Dittmer', 'Tamara G. Grossmann'] | 2022-06-09 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.79458612e-01 -1.13988398e-02 7.72055835e-02 -1.91058353e-01
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cc12fb10-c846-4f14-9e0d-11d917210720 | coconet-a-collaborative-convolutional-network | 1901.09886 | null | https://arxiv.org/abs/1901.09886v4 | https://arxiv.org/pdf/1901.09886v4.pdf | CoCoNet: A Collaborative Convolutional Network | We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the convolutional layers to represent an image as an optimal weighted collaboration of features learned from training samples as a whole rather than one at a time. This gives CoCoNet more power to encode the fine-grained nature of the data with limited samples. We perform a detailed study of the performance with 1-stage and 2-stage transfer learning. The ablation study shows that the proposed method outperforms its constituent parts consistently. CoCoNet also outperforms few state-of-the-art competing methods. Experiments have been performed on the fine-grained bird species classification problem as a representative example, but the method may be applied to other similar tasks. We also introduce a new public dataset for fine-grained species recognition, that of Indian endemic birds and have reported initial results on it. | ['Umapada Pal', 'Steven Mills', 'Brendan McCane', 'Tapabrata Chakraborti'] | 2019-01-28 | null | null | null | null | ['fine-grained-visual-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [-1.12002477e-01 -3.52465451e-01 -8.64940733e-02 -7.88327098e-01
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15116980-89ba-42ba-9586-9965f93f8e5a | growing-regression-forests-by-classification | 1312.6430 | null | http://arxiv.org/abs/1312.6430v2 | http://arxiv.org/pdf/1312.6430v2.pdf | Growing Regression Forests by Classification: Applications to Object Pose Estimation | In this work, we propose a novel node splitting method for regression trees
and incorporate it into the regression forest framework. Unlike traditional
binary splitting, where the splitting rule is selected from a predefined set of
binary splitting rules via trial-and-error, the proposed node splitting method
first finds clusters of the training data which at least locally minimize the
empirical loss without considering the input space. Then splitting rules which
preserve the found clusters as much as possible are determined by casting the
problem into a classification problem. Consequently, our new node splitting
method enjoys more freedom in choosing the splitting rules, resulting in more
efficient tree structures. In addition to the Euclidean target space, we
present a variant which can naturally deal with a circular target space by the
proper use of circular statistics. We apply the regression forest employing our
node splitting to head pose estimation (Euclidean target space) and car
direction estimation (circular target space) and demonstrate that the proposed
method significantly outperforms state-of-the-art methods (38.5% and 22.5%
error reduction respectively). | ['Kota Hara', 'Rama Chellappa'] | 2013-12-22 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 2.95261770e-01 2.46782571e-01 -4.20379847e-01 -5.43394387e-01
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daa7dc85-8c35-4a6d-b1c2-8fc1edd5e2c3 | cqsumdp-a-chatgpt-annotated-resource-for | 2305.06147 | null | https://arxiv.org/abs/2305.06147v1 | https://arxiv.org/pdf/2305.06147v1.pdf | CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia | Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years. However, it has been recently found that this dataset is limited by noise and even most queries in this dataset do not have any relevance to the respective document. In this paper, we present a methodology for cleaning the Debatepedia dataset by leveraging the generative power of large language models to make it suitable for query-focused abstractive summarization. More specifically, we harness the language generation capabilities of ChatGPT to regenerate its queries. We evaluate the effectiveness of the proposed ChatGPT annotated version of the Debatepedia dataset using several benchmark summarization models and demonstrate that the newly annotated version of Debatepedia outperforms the original dataset in terms of both query relevance as well as summary generation quality. We will make this annotated and cleaned version of the dataset publicly available. | ['Jimmy Huang', 'Enamul Hoque', 'Israt Jahan', 'Mizanur Rahman', 'Md Tahmid Rahman Laskar'] | 2023-03-31 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.56751382e-01 6.16911292e-01 -3.08020622e-01 -2.47997120e-02
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49aba34a-8b70-402d-a469-ae867b74eee2 | leveraging-shape-completion-for-3d-siamese | 1903.01784 | null | http://arxiv.org/abs/1903.01784v2 | http://arxiv.org/pdf/1903.01784v2.pdf | Leveraging Shape Completion for 3D Siamese Tracking | Point clouds are challenging to process due to their sparsity, therefore
autonomous vehicles rely more on appearance attributes than pure geometric
features. However, 3D LIDAR perception can provide crucial information for
urban navigation in challenging light or weather conditions. In this paper, we
investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR
point clouds. We design a Siamese tracker that encodes model and candidate
shapes into a compact latent representation. We regularize the encoding by
enforcing the latent representation to decode into an object model shape. We
observe that 3D object tracking and 3D shape completion complement each other.
Learning a more meaningful latent representation shows better discriminatory
capabilities, leading to improved tracking performance. We test our method on
the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94%
Success rate and 81.38% Precision for 3D Object Tracking, with the shape
completion regularization leading to an improvement of 3% in both metrics. | ['Bernard Ghanem', 'Silvio Giancola', 'Jesus Zarzar'] | 2019-03-05 | leveraging-shape-completion-for-3d-siamese-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Giancola_Leveraging_Shape_Completion_for_3D_Siamese_Tracking_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Giancola_Leveraging_Shape_Completion_for_3D_Siamese_Tracking_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-tracking'] | ['computer-vision'] | [-8.61168280e-02 -1.95493251e-01 -4.22093213e-01 -3.77071261e-01
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a3fcc9d1-04f4-4ae6-a5cd-06f8595d96bc | jpeg-steganalysis-based-on-steganographic | 2302.02276 | null | https://arxiv.org/abs/2302.02276v1 | https://arxiv.org/pdf/2302.02276v1.pdf | JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning | The purpose of image steganalysis is to determine whether the carrier image contains hidden information or not. Since JEPG is the most commonly used image format over social networks, steganalysis in JPEG images is also the most urgently needed to be explored. However, in order to detect whether secret information is hidden within JEPG images, the majority of existing algorithms are designed in conjunction with the popular computer vision related networks, without considering the key characteristics appeared in image steganalysis. It is crucial that the steganographic signal, as an extremely weak signal, can be enhanced during its representation learning process. Motivated by this insight, in this paper, we introduce a novel representation learning algorithm for JPEG steganalysis that is mainly consisting of a graph attention learning module and a feature enhancement module. The graph attention learning module is designed to avoid global feature loss caused by the local feature learning of convolutional neural network and reliance on depth stacking to extend the perceptual domain. The feature enhancement module is applied to prevent the stacking of convolutional layers from weakening the steganographic information. In addition, pretraining as a way to initialize the network weights with a large-scale dataset is utilized to enhance the ability of the network to extract discriminative features. We advocate pretraining with ALASKA2 for the model trained with BOSSBase+BOWS2. The experimental results indicate that the proposed algorithm outperforms previous arts in terms of detection accuracy, which has verified the superiority and applicability of the proposed work. | ['Hanzhou Wu', 'Zhiguang Yang', 'Qiyun Liu'] | 2023-02-05 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 7.69665718e-01 1.57179058e-01 3.35868485e-02 2.28620157e-01
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079fb356-30ee-472c-8047-550d22f30c49 | automatic-sound-event-detection-and | 2301.02214 | null | https://arxiv.org/abs/2301.02214v2 | https://arxiv.org/pdf/2301.02214v2.pdf | Automatic Sound Event Detection and Classification of Great Ape Calls Using Neural Networks | We present a novel approach to automatically detect and classify great ape calls from continuous raw audio recordings collected during field research. Our method leverages deep pretrained and sequential neural networks, including wav2vec 2.0 and LSTM, and is validated on three data sets from three different great ape lineages (orangutans, chimpanzees, and bonobos). The recordings were collected by different researchers and include different annotation schemes, which our pipeline preprocesses and trains in a uniform fashion. Our results for call detection and classification attain high accuracy. Our method is aimed to be generalizable to other animal species, and more generally, sound event detection tasks. To foster future research, we make our pipeline and methods publicly available. | ['Steven Moran', 'Adriano R. Lameira', 'Isaac Schamberg', 'Adrian Soldati', 'Zifan Jiang'] | 2023-01-05 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 4.09862131e-01 -4.27586287e-01 2.69856244e-01 -4.61782724e-01
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a46dc8fe-a50d-4b6d-9a26-8a47c40d6886 | speech-denoising-using-only-single-noisy | 2111.00242 | null | https://arxiv.org/abs/2111.00242v4 | https://arxiv.org/pdf/2111.00242v4.pdf | Self-Supervised Speech Denoising Using Only Noisy Audio Signals | In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://github.com/liqingchunnnn/Only-Noisy-Training | ['Lotfi Senhadji', 'Lei LI', 'Huazhong Shu', 'Guanyu Yang', 'Jiasong Wu', 'Qingchun Li'] | 2021-10-30 | null | null | null | null | ['audio-denoising', 'speech-denoising'] | ['audio', 'speech'] | [ 2.54058897e-01 -2.21111387e-01 4.80855405e-01 -4.81714427e-01
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82745c42-2af3-4d29-a209-51e95a2226ab | quantifying-natural-and-artificial | 1412.6703 | null | http://arxiv.org/abs/1412.6703v2 | http://arxiv.org/pdf/1412.6703v2.pdf | Quantifying Natural and Artificial Intelligence in Robots and Natural Systems with an Algorithmic Behavioural Test | One of the most important aims of the fields of robotics, artificial
intelligence and artificial life is the design and construction of systems and
machines as versatile and as reliable as living organisms at performing high
level human-like tasks. But how are we to evaluate artificial systems if we are
not certain how to measure these capacities in living systems, let alone how to
define life or intelligence? Here I survey a concrete metric towards measuring
abstract properties of natural and artificial systems, such as the ability to
react to the environment and to control one's own behaviour. | ['Hector Zenil'] | 2014-12-20 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 6.82351217e-02 3.70727807e-01 3.89817685e-01 1.13392428e-01
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1d71a1db-bebd-4ab2-afd9-d494da813f5e | signing-at-scale-learning-to-co-articulate | 2203.15354 | null | https://arxiv.org/abs/2203.15354v1 | https://arxiv.org/pdf/2203.15354v1.pdf | Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production | Sign languages are visual languages, with vocabularies as rich as their spoken language counterparts. However, current deep-learning based Sign Language Production (SLP) models produce under-articulated skeleton pose sequences from constrained vocabularies and this limits applicability. To be understandable and accepted by the deaf, an automatic SLP system must be able to generate co-articulated photo-realistic signing sequences for large domains of discourse. In this work, we tackle large-scale SLP by learning to co-articulate between dictionary signs, a method capable of producing smooth signing while scaling to unconstrained domains of discourse. To learn sign co-articulation, we propose a novel Frame Selection Network (FS-Net) that improves the temporal alignment of interpolated dictionary signs to continuous signing sequences. Additionally, we propose SignGAN, a pose-conditioned human synthesis model that produces photo-realistic sign language videos direct from skeleton pose. We propose a novel keypoint-based loss function which improves the quality of synthesized hand images. We evaluate our SLP model on the large-scale meineDGS (mDGS) corpus, conducting extensive user evaluation showing our FS-Net approach improves co-articulation of interpolated dictionary signs. Additionally, we show that SignGAN significantly outperforms all baseline methods for quantitative metrics, human perceptual studies and native deaf signer comprehension. | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sign-language-production'] | ['natural-language-processing'] | [ 3.63652259e-01 2.12537035e-01 -2.14731619e-01 -1.67699605e-01
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1650a335-f7e6-44a0-ac10-ff97bf7f45e4 | graph-neural-network-for-spatiotemporal-data | 2306.00012 | null | https://arxiv.org/abs/2306.00012v1 | https://arxiv.org/pdf/2306.00012v1.pdf | Graph Neural Network for spatiotemporal data: methods and applications | In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed. | ['Liang Zhao', 'Xiaoyun Gong', 'Minxing Zhang', 'Zhenke Liu', 'Dazhou Yu', 'Yun Li'] | 2023-05-30 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.09843798e-02 -2.62520492e-01 -3.56087446e-01 -1.64660200e-01
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49367560-a41b-4b96-9f83-417b6ef9a3f6 | practical-hidden-voice-attacks-against-speech | 1904.05734 | null | http://arxiv.org/abs/1904.05734v1 | http://arxiv.org/pdf/1904.05734v1.pdf | Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems | Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks. | ['Patrick Traynor', 'Hadi Abdullah', 'Kevin R. B. Butler', 'Joseph Wilson', 'Christian Peeters', 'Washington Garcia'] | 2019-03-18 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.15169990e-01 -1.19757667e-01 3.05030107e-01 1.84039935e-01
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33da2562-c0e3-48ee-acbf-f03707a32728 | better-generalized-few-shot-learning-even | 2211.16095 | null | https://arxiv.org/abs/2211.16095v2 | https://arxiv.org/pdf/2211.16095v2.pdf | Better Generalized Few-Shot Learning Even Without Base Data | This paper introduces and studies zero-base generalized few-shot learning (zero-base GFSL), which is an extreme yet practical version of few-shot learning problem. Motivated by the cases where base data is not available due to privacy or ethical issues, the goal of zero-base GFSL is to newly incorporate the knowledge of few samples of novel classes into a pretrained model without any samples of base classes. According to our analysis, we discover the fact that both mean and variance of the weight distribution of novel classes are not properly established, compared to those of base classes. The existing GFSL methods attempt to make the weight norms balanced, which we find helps only the variance part, but discard the importance of mean of weights particularly for novel classes, leading to the limited performance in the GFSL problem even with base data. In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base samples and thereby achieve a satisfactory performance on both novel and base classes. Our experimental results somewhat surprisingly show that the proposed zero-base GFSL method that does not utilize any base samples even outperforms the existing GFSL methods that make the best use of base data. Our implementation is available at: https://github.com/bigdata-inha/Zero-Base-GFSL. | ['Seong-Woong Kim', 'Dong-Wan Choi'] | 2022-11-29 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 8.76144618e-02 7.55905360e-03 -2.60030955e-01 -2.02138275e-01
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ef80dcdf-2e57-43c9-bc0d-b50c15ca8fec | automatic-generation-of-alternative-starting | 1411.4023 | null | http://arxiv.org/abs/1411.4023v1 | http://arxiv.org/pdf/1411.4023v1.pdf | Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games | Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role
not only in the development of mathematical and logical skills, but also in the
emotional and social development. In this paper, we address the problem of
generating targeted starting positions for such games. This can facilitate new
approaches for bringing novice players to mastery, and also leads to discovery
of interesting game variants. We present an approach that generates starting
states of varying hardness levels for player~$1$ in a two-player board game,
given rules of the board game, the desired number of steps required for
player~$1$ to win, and the expertise levels of the two players. Our approach
leverages symbolic methods and iterative simulation to efficiently search the
extremely large state space. We present experimental results that include
discovery of states of varying hardness levels for several simple grid-based
board games. The presence of such states for standard game variants like $4
\times 4$ Tic-Tac-Toe opens up new games to be played that have never been
played as the default start state is heavily biased. | ['Sumit Gulwani', 'Krishnendu Chatterjee', 'Umair Z. Ahmed'] | 2014-11-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.42361242e-01 4.90811437e-01 2.83093750e-01 1.95862681e-01
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0d74b945-1af6-4fc5-a8f9-ad1c1f8fecbd | a-perspectival-mirror-of-the-elephant | 2303.16281 | null | https://arxiv.org/abs/2303.16281v2 | https://arxiv.org/pdf/2303.16281v2.pdf | A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube | Contrary to Google Search's mission of delivering information from "many angles so you can form your own understanding of the world," we find that Google and its most prominent returned results - Wikipedia and YouTube - simply reflect a narrow set of cultural stereotypes tied to the search language for complex topics like "Buddhism," "Liberalism," "colonization," "Iran" and "America." Simply stated, they present, to varying degrees, distinct information across the same search in different languages, a phenomenon we call 'language bias.' This paper presents evidence and analysis of language bias and discusses its larger social implications. Instead of presenting a global picture of a complex topic, our online searches and emerging tools like ChatGPT turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. Piecing together a genuine depiction of the elephant is a challenging and important endeavor that will require collaborative efforts from scholars in both the humanities and technology. | ['Michael D. Smith', 'Michael J. Puett', 'Queenie Luo'] | 2023-03-28 | null | null | null | null | ['culture'] | ['speech'] | [-3.19408119e-01 -1.61341354e-02 -7.47887492e-01 2.17179894e-01
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c0108558-f280-4ef8-a9f1-f7b3076a5cc0 | selective-query-guided-debiasing-network-for | 2210.08714 | null | https://arxiv.org/abs/2210.08714v2 | https://arxiv.org/pdf/2210.08714v2.pdf | Selective Query-guided Debiasing for Video Corpus Moment Retrieval | Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query. Existing retrieval systems tend to rely on retrieval bias as a shortcut and thus, fail to sufficiently learn multi-modal interactions between query and video. This retrieval bias stems from learning frequent co-occurrence patterns between query and moments, which spuriously correlate objects (e.g., a pencil) referred in the query with moments (e.g., scene of writing with a pencil) where the objects frequently appear in the video, such that they converge into biased moment predictions. Although recent debiasing methods have focused on removing this retrieval bias, we argue that these biased predictions sometimes should be preserved because there are many queries where biased predictions are rather helpful. To conjugate this retrieval bias, we propose a Selective Query-guided Debiasing network (SQuiDNet), which incorporates the following two main properties: (1) Biased Moment Retrieval that intentionally uncovers the biased moments inherent in objects of the query and (2) Selective Query-guided Debiasing that performs selective debiasing guided by the meaning of the query. Our experimental results on three moment retrieval benchmarks (i.e., TVR, ActivityNet, DiDeMo) show the effectiveness of SQuiDNet and qualitative analysis shows improved interpretability. | ['Chang D. Yoo', 'Hee Suk Yoon', 'Junyeong Kim', 'Dahyun Kim', 'Eunseop Yoon', 'Ji Woo Hong', 'Sunjae Yoon'] | 2022-10-17 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-4.53701466e-02 -5.75596273e-01 -7.76936769e-01 -4.64967787e-02
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7466e0f8-20e7-4c4f-a463-e0eb2701c04c | a-jet-tagging-algorithm-of-graph-network-with | 2210.13869 | null | https://arxiv.org/abs/2210.13869v3 | https://arxiv.org/pdf/2210.13869v3.pdf | A jet tagging algorithm of graph network with HaarPooling message passing | Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extract the features of graph, but also embed additional information obtained by clustering of k-means of different particle observables. We construct Haarpooling from three different observables: absolute energy $\log E$, transverse momentum $\log p_T$ and relative coordinates $(\Delta\eta,\Delta\phi)$, then discuss their impacts on the tagging and compare the results with those obtained via message passing neutral network (MPNN) and ParticleNet (PN). The results show that an appropriate selection of information for HaarPooling enhance the accuracy of quark-gluon tagging, for adding extra information of $\log P_T$ to the HMPNet outperforms all the others, meanwhile adding relative coordinates information $(\Delta\eta,\Delta\phi)$ is not very beneficial. | ['Wei Li', 'Feiyi Liu', 'Fei Ma'] | 2022-10-25 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-8.29376519e-01 8.47915411e-02 1.43447176e-01 -2.02664837e-01
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422c59cc-a25c-4a79-9599-e0b04d559d11 | parallax-estimation-for-push-frame-satellite | 2102.02301 | null | https://arxiv.org/abs/2102.02301v1 | https://arxiv.org/pdf/2102.02301v1.pdf | Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products | Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves during the acquisition of the burst, elevation changes in the scene translate into noticeable parallax. This parallax hinders the results of the restoration. To cope with this issue, we propose a novel parallax estimation method. The method is composed of a linear Plane+Parallax decomposition of the apparent motion and a multi-frame optical flow algorithm that exploits all frames simultaneously. Using SkySat L1A images, we show that the estimated per-pixel displacements are important for applying multi-frame super-resolution on scenes containing elevation changes and that can also be used to estimate a coarse 3D surface model. | ['Gabriele Facciolo', 'Thibaud Ehret', 'Jérémy Anger'] | 2021-02-03 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.70562494e-01 -3.22985172e-01 2.31733248e-01 -1.43523544e-01
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7b21fd13-a5bf-42f6-8080-010535a40318 | on-device-evaluation-toolkit-for-machine | 2306.14574 | null | https://arxiv.org/abs/2306.14574v1 | https://arxiv.org/pdf/2306.14574v1.pdf | On-Device Evaluation Toolkit for Machine Learning on Heterogeneous Low-Power System-on-Chip | Network delays, throughput bottlenecks and privacy issues push Artificial Intelligence of Things (AIoT) designers towards evaluating the feasibility of moving model training and execution (inference) as near as possible to the terminals. Meanwhile, results from the TinyML community demonstrate that, in some cases, it is possible to execute model inference directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, researchers and practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of moving execution of arbitrary models to arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of AIoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of a wide variety of models, on a wide variety of low-power boards, based on popular microcontroller architectures (ARM Cortex-M and RISC-V). U-TOE thus allows easily reproducible and customisable comparative evaluation experiments in this domain, on a wide variety of IoT hardware all-at-once. The availability of a toolkit such as U-TOE is desirable to accelerate the field of AIoT, towards fully exploiting the potential of edge computing. | ['Emmanuel Baccelli', 'Kaspar Schleiser', 'Koen Zandberg', 'Zhaolan Huang'] | 2023-06-26 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-2.71086216e-01 9.04681385e-02 -2.27999449e-01 -2.70738304e-01
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a7f5d83e-644b-4784-9d6c-2d36d9803606 | depthwise-separable-temporal-convolutional | null | null | https://ieeexplore.ieee.org/document/9320343 | https://ieeexplore.ieee.org/document/9320343 | Depthwise Separable Temporal Convolutional Network for Action Segmentation | Fine-grained temporal action segmentation in long,
untrimmed RGB videos is a key topic in visual human-
machine interaction. Recent temporal convolution based
approaches either use encoder-decoder(ED) architecture or
dilations with doubling factor in consecutive convolution
layers to segment actions in videos. However ED networks
operate on low temporal resolution and the dilations in suc-
cessive layers cause gridding artifacts problem. We propose
depthwise separable temporal convolution network (DS-
TCN) that operates on full temporal resolution and with re-
duced gridding effects. The basic component of DS-TCN
is residual depthwise dilated block (RDDB). We explore the
trade-off between large kernels and small dilation rates us-
ing RDDB. We show that our DS-TCN is capable of captur-
ing long-term dependencies as well as local temporal cues
efficiently. Our evaluation on three benchmark datasets,
GTEA, 50Salads, and Breakfast demonstrates that DS-TCN
outperforms the existing ED-TCN and dilation based TCN
baselines even with comparatively fewer parameters. | ['Heiko Neumann', 'Wolfgang Mader', 'Christian Jarvers', 'Basavaraj Hampiholi'] | 2021-01-19 | null | null | null | 2020-international-conference-on-3d-vision | ['action-segmentation'] | ['computer-vision'] | [ 2.15366542e-01 -1.97692692e-01 -2.88782090e-01 -4.12907124e-01
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b3b6e7b7-0a0b-4252-8276-a419c887414d | sparse-subspace-clustering-in-diverse | 2206.07602 | null | https://arxiv.org/abs/2206.07602v2 | https://arxiv.org/pdf/2206.07602v2.pdf | Sparse Subspace Clustering in Diverse Multiplex Network Model | The paper considers the DIverse MultiPLEx (DIMPLE) network model, introduced in Pensky and Wang (2021), where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all layers can be partitioned into groups with the same community structures, although the layers in the same group may have different matrices of block connection probabilities. The DIMPLE model generalizes a multitude of papers that study multilayer networks with the same community structures in all layers, as well as the Mixture Multilayer Stochastic Block Model (MMLSBM), where the layers in the same group have identical matrices of block connection probabilities. While Pensky and Wang (2021) applied spectral clustering to the proxy of the adjacency tensor, the present paper uses Sparse Subspace Clustering (SSC) for identifying groups of layers with identical community structures. Under mild conditions, the latter leads to the strongly consistent between-layer clustering. In addition, SSC allows to handle much larger networks than methodology of Pensky and Wang (2021), and is perfectly suitable for application of parallel computing. | ['Marianna Pensky', 'Majid Noroozi'] | 2022-06-15 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.03628645e-02 -4.53216285e-02 -2.22263902e-01 2.12201238e-01
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87d7cf1d-1362-420f-b42c-bdae9d91cd4a | voxel-level-importance-maps-for-interpretable | 2108.05388 | null | https://arxiv.org/abs/2108.05388v1 | https://arxiv.org/pdf/2108.05388v1.pdf | Voxel-level Importance Maps for Interpretable Brain Age Estimation | Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative diseases. For this purpose accurate prediction is important but the localisation of the areas that play a significant role in the prediction is also crucial, in order to gain clinicians' trust and reassurance about the performance of a prediction model. Most interpretability methods are focused on classification tasks and cannot be directly transferred to regression tasks. In this study, we focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We interpret its predictions by extracting importance maps, which discover the parts of the brain that are the most important for brain age. In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We average the importance maps of the subjects and end up with a population-based importance map, which displays the regions of the brain that are influential for the task. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature, highlighting that the hippocampus and the ventricles are the most relevant regions for brain aging. | ['Daniel Rueckert', 'Alexander Hammers', 'Vasileios Baltatzis', 'Kyriaki-Margarita Bintsi'] | 2021-08-11 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.78117618e-01 2.95243740e-01 3.11848551e-01 -3.91718477e-01
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67d27c6b-0582-493d-99a6-12201fd2c508 | tandem-tracking-and-dense-mapping-in-real | 2111.07418 | null | https://arxiv.org/abs/2111.07418v1 | https://arxiv.org/pdf/2111.07418v1.pdf | TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo | In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth maps rendered from a global model that is built incrementally from dense depth predictions. To predict the dense depth maps, we propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire active keyframe window by hierarchically constructing 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Finally, the predicted depth maps are fused into a consistent global map represented as a truncated signed distance function (TSDF) voxel grid. Our experimental results show that TANDEM outperforms other state-of-the-art traditional and learning-based monocular visual odometry (VO) methods in terms of camera tracking. Moreover, TANDEM shows state-of-the-art real-time 3D reconstruction performance. | ['Daniel Cremers', 'Niclas Zeller', 'Nan Yang', 'Lukas Koestler'] | 2021-11-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.06314877e-01 -1.47026286e-01 7.41305649e-02 -3.24908465e-01
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14cb2198-239b-4da0-89a6-c79bbf3419ae | longformer-the-long-document-transformer | 2004.05150 | null | https://arxiv.org/abs/2004.05150v2 | https://arxiv.org/pdf/2004.05150v2.pdf | Longformer: The Long-Document Transformer | Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset. | ['Iz Beltagy', 'Matthew E. Peters', 'Arman Cohan'] | 2020-04-10 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 4.18330282e-01 1.68609649e-01 7.63562992e-02 -3.19659144e-01
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3a4aa586-844b-4204-af63-ac2a165c086d | guided-interactive-video-object-segmentation | 2104.10386 | null | https://arxiv.org/abs/2104.10386v1 | https://arxiv.org/pdf/2104.10386v1.pdf | Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps | We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap. | ['Chang-Su Kim', 'Yeong Jun Koh', 'Yuk Heo'] | 2021-04-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.pdf | cvpr-2021-1 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [-2.06246570e-01 4.12960583e-03 -2.31236100e-01 -5.20318449e-01
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f675bf15-a4d1-4fe2-a3cb-e54cdf2314c0 | using-open-ended-stressor-responses-to | 2211.07932 | null | https://arxiv.org/abs/2211.07932v1 | https://arxiv.org/pdf/2211.07932v1.pdf | Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics | Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporting stressors across demographic groups. We train language models using self-reported stressors to predict depressive symptoms, finding a relationship between stressors and depression. Finally, we find that differences in stressors translate to downstream performance differences across demographic groups. | ['Philip Resnik', 'Mark Dredze', 'Carlos Aguirre'] | 2022-11-15 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-3.67780447e-01 3.30178104e-02 -7.57938564e-01 -6.85995400e-01
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f19c6542-0c6b-4052-ae02-020d10a9b38c | sequential-decision-making-for-active-object | 2110.11524 | null | https://arxiv.org/abs/2110.11524v4 | https://arxiv.org/pdf/2110.11524v4.pdf | Sequential Voting with Relational Box Fields for Active Object Detection | A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accurately localize the active object, any method must reason using information encoded by each image pixel, such as whether it belongs to the hand, the object, or the background. To leverage each pixel as evidence to determine the bounding box of the active object, we propose a pixel-wise voting function. Our pixel-wise voting function takes an initial bounding box as input and produces an improved bounding box of the active object as output. The voting function is designed so that each pixel inside of the input bounding box votes for an improved bounding box, and the box with the majority vote is selected as the output. We call the collection of bounding boxes generated inside of the voting function, the Relational Box Field, as it characterizes a field of bounding boxes defined in relationship to the current bounding box. While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object. Therefore, we repeatedly apply the voting function to sequentially improve the location of the bounding box. However, since it is known that repeatedly applying a one-step predictor (i.e., auto-regressive processing with our voting function) can cause a data distribution shift, we mitigate this issue using reinforcement learning (RL). We adopt standard RL to learn the voting function parameters and show that it provides a meaningful improvement over a standard supervised learning approach. We perform experiments on two large-scale datasets: 100DOH and MECCANO, improving AP50 performance by 8% and 30%, respectively, over the state of the art. | ['Kris M. Kitani', 'Xingyu Liu', 'Qichen Fu'] | 2021-10-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['active-object-detection'] | ['computer-vision'] | [ 4.69832301e-01 -4.01876904e-02 -3.12628984e-01 -9.33016241e-02
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e32478d2-8d7c-4c8d-a642-d93140d01dd2 | single-image-deraining-a-comprehensive | 1903.08558 | null | http://arxiv.org/abs/1903.08558v1 | http://arxiv.org/pdf/1903.08558v1.pdf | Single Image Deraining: A Comprehensive Benchmark Analysis | We present a comprehensive study and evaluation of existing single image
deraining algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world rainy images.This dataset highlights diverse data
sources and image contents, and is divided into three subsets (rain streak,
rain drop, rain and mist), each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on the
dataset shed light on the comparisons and limitations of state-of-the-art
deraining algorithms, and suggest promising future directions. | ['Roberto Cesar-Junior', 'Siyuan Li', 'Xiaojie Guo', 'Iago Breno Araujo', 'Zhangyang Wang', 'Wenqi Ren', 'Xiaochun Cao', 'Roberto Hirata Junior', 'Eric K. Tokuda', 'Jiawan Zhang'] | 2019-03-20 | single-image-deraining-a-comprehensive-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Single_Image_Deraining_A_Comprehensive_Benchmark_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Single_Image_Deraining_A_Comprehensive_Benchmark_Analysis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [ 2.47689620e-01 -4.87196267e-01 3.04509401e-01 -6.49823666e-01
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3cf8e78f-88b9-41c5-a750-800d64c5cdbd | exploring-the-versatility-of-zero-shot-clip | 2306.01111 | null | https://arxiv.org/abs/2306.01111v1 | https://arxiv.org/pdf/2306.01111v1.pdf | Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification | Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans and proceeding to ILD classification using "patch montages". Furthermore, we investigate how domain adaptive pretraining (DAPT) CLIP with task-specific images (CT "patch montages" extracted with ILD-specific prompts for CLIP) and/or text (lung-specific sections of radiology reports) affects downstream ILD classification performance. By leveraging CLIP-extracted "patch montages" and DAPT, we achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data. This work highlights the versatility and potential of multimodal models like CLIP for medical image classification tasks where labeled data is scarce. | ['Curtis Langlotz', 'Rishi Raj', 'Neha Simha', 'Haiwei Henry Guo', 'Malgorzata Polacin', 'Maayane Attias', 'Christian Bluethgen', 'Cara Van Uden'] | 2023-06-01 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 7.27906168e-01 7.89845660e-02 -4.26436931e-01 -4.87762064e-01
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ac4e00d7-ffe4-4200-896c-c2602a0a5558 | cascading-and-direct-approaches-to | 2303.08809 | null | https://arxiv.org/abs/2303.08809v2 | https://arxiv.org/pdf/2303.08809v2.pdf | Cascading and Direct Approaches to Unsupervised Constituency Parsing on Spoken Sentences | Past work on unsupervised parsing is constrained to written form. In this paper, we present the first study on unsupervised spoken constituency parsing given unlabeled spoken sentences and unpaired textual data. The goal is to determine the spoken sentences' hierarchical syntactic structure in the form of constituency parse trees, such that each node is a span of audio that corresponds to a constituent. We compare two approaches: (1) cascading an unsupervised automatic speech recognition (ASR) model and an unsupervised parser to obtain parse trees on ASR transcripts, and (2) direct training an unsupervised parser on continuous word-level speech representations. This is done by first splitting utterances into sequences of word-level segments, and aggregating self-supervised speech representations within segments to obtain segment embeddings. We find that separately training a parser on the unpaired text and directly applying it on ASR transcripts for inference produces better results for unsupervised parsing. Additionally, our results suggest that accurate segmentation alone may be sufficient to parse spoken sentences accurately. Finally, we show the direct approach may learn head-directionality correctly for both head-initial and head-final languages without any explicit inductive bias. | ['Hung-Yi Lee', 'Cheng-I Lai', 'Yuan Tseng'] | 2023-03-15 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 6.98758245e-01 7.61666358e-01 -1.43164784e-01 -1.13647413e+00
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d11cd9ac-0a85-4a36-9850-40e692317bc1 | segmentation-guided-deep-hdr-deghosting | 2207.01229 | null | https://arxiv.org/abs/2207.01229v1 | https://arxiv.org/pdf/2207.01229v1.pdf | Segmentation Guided Deep HDR Deghosting | We present a motion segmentation guided convolutional neural network (CNN) approach for high dynamic range (HDR) image deghosting. First, we segment the moving regions in the input sequence using a CNN. Then, we merge static and moving regions separately with different fusion networks and combine fused features to generate the final ghost-free HDR image. Our motion segmentation guided HDR fusion approach offers significant advantages over existing HDR deghosting methods. First, by segmenting the input sequence into static and moving regions, our proposed approach learns effective fusion rules for various challenging saturation and motion types. Second, we introduce a novel memory network that accumulates the necessary features required to generate plausible details in the saturated regions. The proposed method outperforms nine existing state-of-the-art methods on two publicly available datasets and generates visually pleasing ghost-free HDR results. We also present a large-scale motion segmentation dataset of 3683 varying exposure images to benefit the research community. | ['R. Venkatesh Babu', 'Susmit Agrawal', 'K. Ram Prabhakar'] | 2022-07-04 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.08959925e-01 -2.87860483e-01 -1.03312314e-01 -2.31286302e-01
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48980dc2-d80a-4445-9e67-0c9d32c9536d | cross-domain-toxic-spans-detection | 2306.09642 | null | https://arxiv.org/abs/2306.09642v1 | https://arxiv.org/pdf/2306.09642v1.pdf | Cross-Domain Toxic Spans Detection | Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans under cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned language models. Our findings indicate that a simple method using off-the-shelf lexicons performs best in the cross-domain setup. The cross-domain error analysis suggests that (1) rationale extraction methods are prone to false negatives, while (2) language models, despite performing best for the in-domain case, recall fewer explicitly toxic words than lexicons and are prone to certain types of false positives. Our code is publicly available at: https://github.com/sfschouten/toxic-cross-domain. | ['Ilia Markov', 'Piek Vossen', 'Wondimagegnhue Tufa', 'Baran Barbarestani', 'Stefan F. Schouten'] | 2023-06-16 | null | null | null | null | ['toxic-spans-detection'] | ['natural-language-processing'] | [-1.65654421e-01 -1.76292196e-01 -4.23141837e-01 -5.93236014e-02
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ad55633a-34d5-4660-9112-720b43d85c7a | dialoguegcn-a-graph-convolutional-neural | 1908.11540 | null | https://arxiv.org/abs/1908.11540v1 | https://arxiv.org/pdf/1908.11540v1.pdf | DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets. | ['Alexander Gelbukh', 'Niyati Chhaya', 'Soujanya Poria', 'Navonil Majumder', 'Deepanway Ghosal'] | 2019-08-30 | dialoguegcn-a-graph-convolutional-neural-1 | https://aclanthology.org/D19-1015 | https://aclanthology.org/D19-1015.pdf | ijcnlp-2019-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 6.45023510e-02 2.38313228e-01 6.78651109e-02 -4.86129880e-01
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867647d8-737c-4c03-9281-67d85ad9de5d | exploring-contextual-relationships-for | 2207.04693 | null | https://arxiv.org/abs/2207.04693v2 | https://arxiv.org/pdf/2207.04693v2.pdf | Exploring Contextual Relationships for Cervical Abnormal Cell Detection | Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD. | ['Jianxin Wang', 'Jianfeng Liu', 'Yun Du', 'Liyan Liao', 'Hulin Kuang', 'Qing Liu', 'Shuo Feng', 'Yixiong Liang'] | 2022-07-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.89353004e-01 -8.21990445e-02 -1.92364991e-01 -8.18828046e-02
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db2d458f-ba7c-44e3-b955-e78b2ae19118 | pesto-a-post-user-fusion-network-for-rumour | null | null | https://openreview.net/forum?id=vVNYde75l4m | https://openreview.net/pdf?id=vVNYde75l4m | PESTO: A Post-User Fusion Network for Rumour Detection on Social Media | Rumour detection on social media is an important topic due to the challenges of misinformation propagation and slow verification of misleading information.
Most previous work focus on the response posts on social media, ignoring the useful characteristics of involved users and their relations.
In this paper, we propose a novel framework, Post-User Fusion Network (PESTO), which models the patterns of rumours from both post diffusion and user social networks. Specifically, we propose a novel Chronologically-masked Transformer architecture to model both temporal sequence and diffusion structure of rumours, and apply a Relational Graph Convolutional Network to model the social relations of involved users, with a fusion network based on self-attention mechanism to incorporate the two aspects. Additionally, two data augmentation techniques are leveraged to improve the robustness and accuracy of our models. Empirical results on several benchmarks show the superiority of the proposed method. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['rumour-detection'] | ['natural-language-processing'] | [-1.69949085e-01 2.88818516e-02 -2.10430712e-01 -2.29210794e-01
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9764903d-d49d-40a8-8c09-89ec319540ad | multi-grained-vision-language-pre-training | 2111.08276 | null | https://arxiv.org/abs/2111.08276v3 | https://arxiv.org/pdf/2111.08276v3.pdf | Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts | Most existing methods in vision language pre-training rely on object-centric features extracted through object detection and make fine-grained alignments between the extracted features and texts. It is challenging for these methods to learn relations among multiple objects. To this end, we propose a new method called X-VLM to perform `multi-grained vision language pre-training.' The key to learning multi-grained alignments is to locate visual concepts in the image given the associated texts, and in the meantime align the texts with the visual concepts, where the alignments are in multi-granularity. Experimental results show that X-VLM effectively leverages the learned multi-grained alignments to many downstream vision language tasks and consistently outperforms state-of-the-art methods. | ['Hang Li', 'Xinsong Zhang', 'Yan Zeng'] | 2021-11-16 | null | null | null | null | ['referring-expression-segmentation', 'open-vocabulary-attribute-detection'] | ['computer-vision', 'computer-vision'] | [ 2.25289688e-02 -2.64378011e-01 -2.54738480e-01 -5.66756189e-01
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252a1c58-ba55-4151-b2da-d787e3714094 | implicit-subspace-prior-learning-for-dual | 2010.05508 | null | https://arxiv.org/abs/2010.05508v1 | https://arxiv.org/pdf/2010.05508v1.pdf | Implicit Subspace Prior Learning for Dual-Blind Face Restoration | Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology. However, real-world image prior are often hard to simulate with precise mathematical models, which inevitably limits the performance and generalization ability of existing prior-regularized restoration methods. In this paper, we study the problem of face restoration under a more practical ``dual blind'' setting, i.e., without prior assumptions or hand-crafted regularization terms on the degradation profile or image contents. To this end, a novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration, with two key elements: 1) an implicit formulation to circumvent the ill-defined restoration mapping and 2) a subspace prior decomposition and fusion mechanism to dynamically handle inputs at varying degradation levels with consistent high-quality restoration results. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods for a variety of restoration subtasks, including a 3.69db PSNR and 45.8% FID gain against ESRGAN, the 2018 NTIRE SR challenge winner. Overall, we prove that it is possible to capture and utilize prior knowledge without explicitly formulating it, which will help inspire new research paradigms towards low-level vision tasks. | ['Wen Gao', 'Siwei Ma', 'Peiran Ren', 'Shanshe Wang', 'Zhanning Gao', 'Pan Wang', 'Lingbo Yang'] | 2020-10-12 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 6.03931129e-01 -4.57087696e-01 4.70035709e-02 -2.59586424e-01
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ae1fbf17-66e4-4492-8d87-e7e7b4b498d6 | propall-probabilistic-partial-label-learning | 2208.09931 | null | https://arxiv.org/abs/2208.09931v1 | https://arxiv.org/pdf/2208.09931v1.pdf | ProPaLL: Probabilistic Partial Label Learning | Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approaches: it simplifies the training process, improves performance, and can be applied to any deep architecture. Experiments conducted on artificial and real-world datasets indicate that ProPaLL outperforms the existing approaches. | ['Bartosz Zieliński', 'Jacek Tabor', 'Łukasz Struski'] | 2022-08-21 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 8.74199048e-02 2.17503011e-01 -6.60628736e-01 -7.72171021e-01
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c8f07375-129f-4a7b-9d81-d2540accc2e1 | classifying-temporal-relations-by | null | null | https://aclanthology.org/P17-2001 | https://aclanthology.org/P17-2001.pdf | Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths | Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a {``}common root{''} assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.). | ['Yusuke Miyao', 'Fei Cheng'] | 2017-07-01 | null | null | null | acl-2017-7 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-7.87196755e-02 5.62570930e-01 -6.07780695e-01 -5.69088101e-01
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1739ff6a-4940-4d81-b1ad-fd5a037ebd75 | why-are-nlp-models-fumbling-at-elementary-1 | 2205.15683 | null | https://arxiv.org/abs/2205.15683v1 | https://arxiv.org/pdf/2205.15683v1.pdf | Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers | From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we are still miles away from building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last two years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyse why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavour to provide a road-map for future math word problem research. | ['Savitha Sam Abraham', 'Deepak P', 'Marco Fisichella', 'Sairam Gurajada', 'Sowmya S Sundaram'] | 2022-05-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.26318136e-01 -3.47561985e-02 -4.12216969e-02 -3.44196826e-01
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a1739bf4-ad06-47ea-a3fc-3ac4f570e527 | bdcn-semantic-embedding-self-explanatory | null | null | https://aclanthology.org/2021.ccl-1.105 | https://aclanthology.org/2021.ccl-1.105.pdf | BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network | “Building an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy to explain but the accuracy is very low. Deep neural network greatly improves the accuracy of diagnosis but the black box model does not provide transparency and interpretation. In this work we propose a semantic embedding self-explanatory Breast Diagnostic Capsules Network(BDCN). This model is the first to combine the capsule network with semantic embedding for theAI diagnosis of breast tumors using capsules to simulate semantics. We pre-trained the extrac-tion word vector by embedding the semantic tree into the BERT and used the capsule network to improve the semantic representation of multiple heads of attention to construct the extraction feature the capsule network was extended from the computer vision classification task to the text classification task. Simultaneously both the back propagation principle and dynamic routing algorithm are used to realize the local interpretability of the diagnostic model. The experimental results show that this breast diagnosis model improves the model performance and has good interpretability which is more suitable for clinical situations.IntroductionBreast cancer is an important killer threatening women’s health because of rising incidence. Early detection and diagnosis are the key to reduce the mortality rate of breast cancer and improve the quality of life of patients. Mammary gland molybdenum target report contains rich semantic information whichcan directly reflect the results of breast cancer screening (CACA-CBCS 2019) and AI-assisted diagno-sis of breast cancer is an important means. Therefore various diagnostic models were born. Mengwan(2020) used support vector machine(SVM) and Naive Bayes to classify morphological features with anaccuracy of 91.11%. Wei (2009) proposed a classification method of breast cancer based on SVM andthe accuracy of the classifier experiment is 79.25%. These traditional AI diagnoses of breast tumors havelimited data volume and low accuracy. Deep Neural Networks (DNN) enters into the ranks of the diagno-sis of breast tumor. Wang (2019) put forward a kind of based on feature fusion with CNN deep features of breast computer-aided diagnosis methods the accuracy is 92.3%. Zhao (2018) investigated capsule networks with dynamic routing for text classification which proves the feasibility of text categorization. Existing models have poor predictive effect and lack of interpretation which can not meet the clinical needs.” | ['He Jianrong', 'Zhong Keting', 'Chen Dehua'] | null | null | null | null | ccl-2021-8 | ['text-categorization'] | ['natural-language-processing'] | [-3.05714637e-01 6.60003841e-01 -5.49653828e-01 -4.49457586e-01
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54da8cfe-16c9-4f25-81c5-6617986297d4 | onionnet-sharing-features-in-cascaded-deep | 1608.02728 | null | http://arxiv.org/abs/1608.02728v1 | http://arxiv.org/pdf/1608.02728v1.pdf | OnionNet: Sharing Features in Cascaded Deep Classifiers | The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages may add both new layers as well as new feature channels to the previous
ones. Importantly, intermediate feature maps are shared among classifiers,
preventing them from the necessity of being recomputed. To accomplish this, the
model is trained end-to-end in a principled way under a joint loss. We validate
our approach in theory and on a synthetic benchmark. As a result demonstrated
in three applications (patch matching, object detection, and image retrieval),
our cascade can operate significantly faster than both monolithic networks and
traditional cascades without sharing at the cost of marginal decrease in
precision. | ['Nikos Komodakis', 'Martin Simonovsky'] | 2016-08-09 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [-1.45687442e-02 -1.98445797e-01 1.71642173e-02 -5.00224590e-01
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58c00135-85da-45e0-955b-5a01751bdf0f | inter-case-predictive-process-monitoring-a | 2307.00080 | null | https://arxiv.org/abs/2307.00080v1 | https://arxiv.org/pdf/2307.00080v1.pdf | Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning? | Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically. Still, building a model with high predictive power requires both - intrinsic knowledge of how to extract meaningful features from the event log data and a model that captures complex patterns in data. This work builds upon the recent progress in inter-case Predictive Process Monitoring (PPM) and comprehensively benchmarks the impact of inter-case features on prediction accuracy. Moreover, it includes quantum machine learning models, which are expected to provide an advantage over classical models with a scaling amount of feature dimensions. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy and quantum algorithms are indeed competitive in a handful of feature configurations. Yet, as quantum hardware is still in its early stages of development, this paper critically discusses these findings in the light of runtime, noise and the risk to overfit on the training data. Finally, the implementation of an open-source plugin demonstrates the technical feasibility to connect a state-of-the-art workflow engine such as Camunda to an IBM quantum computing cloud service. | ['Carl Corea', 'Patrick Delfmann', 'David Fitzek', 'Stefan Hill'] | 2023-06-30 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.68644685e-01 4.48781811e-02 1.47433892e-01 -2.99764991e-01
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6f1f9570-7df5-40e4-8dee-df5fc5780073 | material-recognition-for-automated-progress | 2006.16344 | null | https://arxiv.org/abs/2006.16344v2 | https://arxiv.org/pdf/2006.16344v2.pdf | Material Recognition for Automated Progress Monitoring using Deep Learning Methods | Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the attention of deep learning and machine vision researchers. However, to create production-ready systems, there is still a long path to cover. Real-world problems such as varying illuminations and reaching acceptable accuracies need to be addressed in order to create robust systems. In this paper, we have addressed these issues and reached a state of the art performance, i.e., 97.35% accuracy rate for this task. Also, a new dataset containing 1231 images of 11 classes taken from several construction sites is gathered and publicly published to help other researchers in this field. | ['Mohammad Tayarani Darbandy', 'Navid Ghassemi', 'Hadi Mahami', 'Roohallah Alizadehsani', 'Saeid Nahavandi', 'Darius Nahavandi', 'Afshin Shoeibi', 'Sadiq Hussain', 'Farnad Nasirzadeh', 'Abbas Khosravi'] | 2020-06-29 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.99836165e-02 -2.81442970e-01 1.36582971e-01 -3.61767203e-01
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e8519cd9-6054-4c19-97eb-b50891ce3f9b | ds-net-dynamic-spatiotemporal-network-for | 2012.04886 | null | https://arxiv.org/abs/2012.04886v3 | https://arxiv.org/pdf/2012.04886v3.pdf | DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection | As moving objects always draw more attention of human eyes, the temporal motive information is always exploited complementarily with spatial information to detect salient objects in videos. Although efficient tools such as optical flow have been proposed to extract temporal motive information, it often encounters difficulties when used for saliency detection due to the movement of camera or the partial movement of salient objects. In this paper, we investigate the complimentary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information. We construct a symmetric two-bypass network to explicitly extract spatial and temporal features. A dynamic weight generator (DWG) is designed to automatically learn the reliability of corresponding saliency branch. And a top-down cross attentive aggregation (CAA) procedure is designed so as to facilitate dynamic complementary aggregation of spatiotemporal features. Finally, the features are modified by spatial attention with the guidance of coarse saliency map and then go through decoder part for final saliency map. Experimental results on five benchmarks VOS, DAVIS, FBMS, SegTrack-v2, and ViSal demonstrate that the proposed method achieves superior performance than state-of-the-art algorithms. The source code is available at https://github.com/TJUMMG/DS-Net. | ['Jiaxiang Wang', 'Jing Liu', 'Weikang Wang', 'Yuting Su'] | 2020-12-09 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 1.03639029e-01 -2.75109798e-01 -3.00063133e-01 -2.11690307e-01
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