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fee21738-9e1a-4016-85c8-6d77fe479f7a | came-context-aware-mixture-of-experts-for | 2208.07109 | null | https://arxiv.org/abs/2208.07109v3 | https://arxiv.org/pdf/2208.07109v3.pdf | Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation | Scene graph generation (SGG) has gained tremendous progress in recent years. However, its underlying long-tailed distribution of predicate classes is a challenging problem. For extremely unbalanced predicate distributions, existing approaches usually construct complicated context encoders to extract the intrinsic relevance of scene context to predicates and complex networks to improve the learning ability of network models for highly imbalanced predicate distributions. To address the unbiased SGG problem, we introduce a simple yet effective method dubbed Context-Aware Mixture-of-Experts (CAME) to improve model diversity and mitigate biased SGG without complicated design. Specifically, we propose to integrate the mixture of experts with a divide and ensemble strategy to remedy the severely long-tailed distribution of predicate classes, which is applicable to the majority of unbiased scene graph generators. The biased SGG is thereby reduced, and the model tends to anticipate more evenly distributed predicate predictions. To differentiate between various predicate distribution levels, experts with the same weights are not sufficiently diverse. In order to enable the network dynamically exploit the rich scene context and further boost the diversity of model, we simply use the built-in module to create a context encoder. The importance of each expert to scene context and each predicate to each expert is dynamically associated with expert weighting (EW) and predicate weighting (PW) strategy. We have conducted extensive experiments on three tasks using the Visual Genome dataset, showing that CAME outperforms recent methods and achieves state-of-the-art performance. Our code will be available publicly. | ['Yangsheng Xu', 'Tin Lun Lam', 'Yuhongze Zhou', 'Liguang Zhou'] | 2022-08-15 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.70853651e-01 2.56601900e-01 -2.37293467e-01 -4.67812747e-01
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d3daaabf-5f96-476f-bca2-f681c17d9d89 | a-vision-transformer-based-approach-to | 2208.07070 | null | https://arxiv.org/abs/2208.07070v2 | https://arxiv.org/pdf/2208.07070v2.pdf | A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals | Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working with large amounts of data, the manual feature extraction approach has the drawback of being inefficient and inaccurate. Data-driven methods like the Deep Learning method have been successfully used in recent years for mechanical intelligent fault detection. Convolutional neural networks (CNNs) were mostly used in earlier research to detect and identify bearing faults. The CNN model, however, suffers from the drawback of having trouble managing fault-time information, which results in a lack of classification results. In this study, bearing defects have been classified using a state-of-the-art Vision Transformer (ViT). Bearing defects were classified using Case Western Reserve University (CWRU) bearing failure laboratory experimental data. The research took into account 13 distinct kinds of defects under 0-load situations in addition to normal bearing conditions. Using the short-time Fourier transform (STFT), the vibration signals were converted into 2D time-frequency images. The 2D time-frequency images are used as input parameters for the ViT. The model achieved an overall accuracy of 98.8%. | ['Minoru Kuribayashi', 'Asad Malik', 'Aquib Iqbal', 'Aeyan Ashraf', 'Abid Hasan Zim'] | 2022-08-15 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-6.43457100e-02 -3.88032049e-01 2.81760454e-01 -1.05856238e-02
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9229ed87-9bda-4581-b8c3-868e46e04910 | embedding-fourier-for-ultra-high-definition | 2302.11831 | null | https://arxiv.org/abs/2302.11831v1 | https://arxiv.org/pdf/2302.11831v1.pdf | Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement | Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaining efficient. Unlike existing methods that address the problem in the spatial domain, we propose a new solution, UHDFour, that embeds Fourier transform into a cascaded network. Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns.Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance. Besides, UHDFour is scalable to UHD images by implementing amplitude and phase enhancement under the low-resolution regime and then adjusting the high-resolution scale with few computations. We also contribute the first real UHD LLIE dataset, \textbf{UHD-LL}, that contains 2,150 low-noise/normal-clear 4K image pairs with diverse darkness and noise levels captured in different scenarios. With this dataset, we systematically analyze the performance of existing LLIE methods for processing UHD images and demonstrate the advantage of our solution. We believe our new framework, coupled with the dataset, would push the frontier of LLIE towards UHD. The code and dataset are available at https://li-chongyi.github.io/UHDFour. | ['Chen Change Loy', 'Ruicheng Feng', 'Shangchen Zhou', 'Zhexin Liang', 'Man Zhou', 'Chun-Le Guo', 'Chongyi Li'] | 2023-02-23 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.90713096e-01 -4.36213195e-01 2.82332361e-01 -2.36138731e-01
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b0345a9f-3761-4d29-bf1d-a9d8b4a44160 | learning-localization-aware-target-confidence | 2204.14093 | null | https://arxiv.org/abs/2204.14093v1 | https://arxiv.org/pdf/2204.14093v1.pdf | Learning Localization-aware Target Confidence for Siamese Visual Tracking | Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA. Within this paradigm, a series of simple, yet effective localization-aware components are introduced, to generate localization-aware target confidence scores. Specifically, with the proposed localization-aware dynamic label (LADL) loss and localization-aware label smoothing (LALS) strategy, collaborative optimization between the classification and regression is achieved, enabling classification scores to be aware of location state, not just appearance similarity. Besides, we propose a separate localization branch, centered on a localization-aware feature aggregation (LAFA) module, to produce location quality scores to further modify the classification scores. Consequently, the resulting target confidence scores, are more discriminative for the location state, allowing accurate prediction boxes tend to be predicted as high scores. Extensive experiments are conducted on six challenging benchmarks, including GOT-10k, TrackingNet, LaSOT, TNL2K, OTB100 and VOT2018. Our SiamLA achieves state-of-the-art performance in terms of both accuracy and efficiency. Furthermore, a stability analysis reveals that our tracking paradigm is relatively stable, implying the paradigm is potential to real-world applications. | ['Zhekang Dong', 'Mingyu Gao', 'Yuxiang Yang', 'Zhiwei He', 'Han Wu', 'Jiahao Nie'] | 2022-04-29 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-5.54113835e-02 -3.15960765e-01 -3.37669641e-01 -3.76753002e-01
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5cfa5407-cd79-4686-a314-cf64ccf2e29a | a-machine-learning-framework-for-authorship | 1912.10204 | null | https://arxiv.org/abs/1912.10204v1 | https://arxiv.org/pdf/1912.10204v1.pdf | A Machine Learning Framework for Authorship Identification From Texts | Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features. | ['Carolyn Penstein Rose', 'Rahul Radhakrishnan Iyer'] | 2019-12-21 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 4.82099541e-02 -8.35619420e-02 -1.73044801e-01 -1.68601915e-01
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757da7fb-27a4-406b-9dae-2feb5bc30fe3 | taln-at-semeval-2016-task-11-modelling | null | null | https://aclanthology.org/S16-1157 | https://aclanthology.org/S16-1157.pdf | TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features | null | ['Luis Espinosa-Anke', "Ahmed Abura{'}ed", 'Francesco Ronzano', 'Horacio Saggion'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e2f18758-0c56-46c6-9681-bc4be31eb74a | deep-sequence-models-for-text-classification | 2207.08880 | null | https://arxiv.org/abs/2207.08880v1 | https://arxiv.org/pdf/2207.08880v1.pdf | Deep Sequence Models for Text Classification Tasks | The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans. | ['Saminu Mohammad Aliyu', 'Musa Bello', 'Abdulkadir Abdullahi', 'Ahmad Muhammad Aminu', 'Abdulrasheed Mustapha', 'Shamsuddeen Hassan Muhammad', 'Sun Yiming', 'Saheed Salahudeen Abdullahi'] | 2022-07-18 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.42411837e-01 -1.22973263e-01 -3.63557965e-01 -5.51877379e-01
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c01532a6-53c9-46cc-b2d7-1aded94c27ba | linguistically-routing-capsule-network-for | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cao_Linguistically_Routing_Capsule_Network_for_Out-of-Distribution_Visual_Question_Answering_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cao_Linguistically_Routing_Capsule_Network_for_Out-of-Distribution_Visual_Question_Answering_ICCV_2021_paper.pdf | Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering | Generalization on out-of-distribution (OOD) test data is an essential but underexplored topic in visual question answering. Current state-of-the-art VQA models often exploit the biased correlation between data and labels, which results in a large performance drop when the test and training data have different distributions. Inspired by the fact that humans can recognize novel concepts by composing existed concepts and capsule network's ability of representing part-whole hierarchies, we propose to use capsules to represent parts and introduce "Linguistically Routing" to merge parts with human-prior hierarchies. Specifically, we first fuse visual features with a single question word as atomic parts. Then we introduce the "Linguistically Routing" to reweight the capsule connections between two layers such that: 1) the lower layer capsules can transfer their outputs to the most compatible higher capsules, and 2) two capsules can be merged if their corresponding words are merged in the question parse tree. The routing process maximizes the above unary and binary potentials across multiple layers and finally carves a tree structure inside the capsule network. We evaluate our proposed routing method on the CLEVR compositional generation test, the VQA-CP2 dataset and the VQAv2 dataset. The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data. | ['Liang Lin', 'Xiaodan Liang', 'Keze Wang', 'Wentao Wan', 'Qingxing Cao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['novel-concepts'] | ['reasoning'] | [-1.88645348e-01 4.45814997e-01 -5.72966179e-03 -5.22256970e-01
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ad30eb76-ba40-418e-8278-6fda8fae248e | spatial-temporal-multi-task-learning-for | 1811.06665 | null | http://arxiv.org/abs/1811.06665v1 | http://arxiv.org/pdf/1811.06665v1.pdf | Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction | Understanding and accurately predicting within-field spatial variability of
crop yield play a key role in site-specific management of crop inputs such as
irrigation water and fertilizer for optimized crop production. However, such a
task is challenged by the complex interaction between crop growth and
environmental and managerial factors, such as climate, soil conditions,
tillage, and irrigation. In this paper, we present a novel Spatial-temporal
Multi-Task Learning algorithms for within-field crop yield prediction in west
Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data
sources to learn different features simultaneously, and to aggregate
spatial-temporal features by introducing a weighted regularizer to the loss
functions. Our comprehensive experimental results consistently outperform the
results of other conventional methods, and suggest a promising approach, which
improves the landscape of crop prediction research fields. | ['Wenxuan Guo', 'Hanxiang Du', 'Zhou Yang', 'Fang Jin', 'Zhe Lin', 'Jia Zhen', 'Long Nguyen'] | 2018-11-16 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.89806949e-02 -9.07089412e-01 -6.92121625e-01 -2.19673395e-01
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d7807c15-0aa2-4750-b83a-341ec82bdc00 | kb4rec-a-dataset-for-linking-knowledge-bases | 1807.11141 | null | https://arxiv.org/abs/1807.11141v7 | https://arxiv.org/pdf/1807.11141v7.pdf | KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems | To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset. | ['Ji-Rong Wen', 'Siqi Ouyang', 'Hongjian Dou', 'Wayne Xin Zhao', 'Jin Huang', 'Gaole He'] | 2018-07-30 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-7.56520152e-01 2.32995465e-01 -8.84942472e-01 -3.41890514e-01
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bf826217-cb4d-400b-baab-f69641a825a4 | uncertainty-quantification-techniques-for | 2201.02067 | null | https://arxiv.org/abs/2201.02067v1 | https://arxiv.org/pdf/2201.02067v1.pdf | Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application | Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and Earth. These systems are tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and collision avoidance operations for objects in low Earth orbit. Even with (assumed) perfect driver forecasts, our incomplete knowledge of the system results in often inaccurate neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of uncertainty. In this work, we propose two techniques to develop nonlinear ML models to predict thermospheric density while providing calibrated uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance for models trained on local and global datasets. This shows that NLPD provides similar results for both techniques but the direct probability method has a much lower computational cost. For the global model regressed on the SET HASDM density database, we achieve errors of 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAMP density dataset, both techniques provide test error on the order of 13%. The CHAMP models (on independent data) are within 2% of perfect calibration for all prediction intervals tested. This model can also be used to obtain global predictions with uncertainties at a given epoch. | ['Piyush M. Mehta', 'Richard J. Licata'] | 2022-01-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.89811581e-01 -2.27850564e-02 -1.48385301e-01 -3.53414595e-01
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0112a9f9-7bd8-482b-b908-614baeb2853c | an-attention-based-graph-neural-network-for | 1912.10832 | null | https://arxiv.org/abs/1912.10832v1 | https://arxiv.org/pdf/1912.10832v1.pdf | An Attention-based Graph Neural Network for Heterogeneous Structural Learning | In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions. | ['Yu-Cheng Lin', 'Zang Li', 'Hantao Guo', 'Xiaoqing Yang', 'Jieping Ye', 'Huiting Hong'] | 2019-12-19 | null | null | null | null | ['heterogeneous-node-classification'] | ['graphs'] | [-9.62723717e-02 4.06969935e-01 -3.79659027e-01 -8.65846723e-02
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57b920cb-7936-4623-9697-96ffdea43599 | auto-fedavg-learnable-federated-averaging-for | 2104.10195 | null | https://arxiv.org/abs/2104.10195v1 | https://arxiv.org/pdf/2104.10195v1.pdf | Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation | Federated learning (FL) enables collaborative model training while preserving each participant's privacy, which is particularly beneficial to the medical field. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. However, non-identical data distribution across clients, known as the non-i.i.d problem in FL, could make this assumption for setting fixed aggregation weights sub-optimal. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models. We disentangle the parameter set into two parts, local model parameters and global aggregation parameters, and update them iteratively with a communication-efficient algorithm. We first show the validity of our approach by outperforming state-of-the-art FL methods for image recognition on a heterogeneous data split of CIFAR-10. Furthermore, we demonstrate our algorithm's effectiveness on two multi-institutional medical image analysis tasks, i.e., COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. | ['Holger Roth', 'Alan Yuille', 'Anna Ierardi', 'Gianpaolo Carrafiello', 'Elvira Stellato', 'Francesca Patella', 'Bradford Wood', 'Baris Turkbey', 'Evrim Turkbey', 'Stephanie Harmon', 'Peng An', 'Hitoshi Mori', 'Hirofumi Obinata', 'Daguang Xu', 'Andriy Myronenko', 'Wenqi Li', 'Dong Yang', 'Yingda Xia'] | 2021-04-20 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-5.28123342e-02 -1.75349340e-01 -3.66771221e-01 -6.15866840e-01
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981e375b-5bae-4f52-8272-daaefeb9aed8 | orthoseg-a-deep-multimodal-convolutional | 1811.07859 | null | http://arxiv.org/abs/1811.07859v2 | http://arxiv.org/pdf/1811.07859v2.pdf | OrthoSeg: A Deep Multimodal Convolutional Neural Network for Semantic Segmentation of Orthoimagery | This paper addresses the task of semantic segmentation of orthoimagery using
multimodal data e.g. optical RGB, infrared and digital surface model. We
propose a deep convolutional neural network architecture termed OrthoSeg for
semantic segmentation using multimodal, orthorectified and coregistered data.
We also propose a training procedure for supervised training of OrthoSeg. The
training procedure complements the inherent architectural characteristics of
OrthoSeg for preventing complex co-adaptations of learned features, which may
arise due to probable high dimensionality and spatial correlation in multimodal
and/or multispectral coregistered data. OrthoSeg consists of parallel encoding
networks for independent encoding of multimodal feature maps and a decoder
designed for efficiently fusing independently encoded multimodal feature maps.
A softmax layer at the end of the network uses the features generated by the
decoder for pixel-wise classification. The decoder fuses feature maps from the
parallel encoders locally as well as contextually at multiple scales to
generate per-pixel feature maps for final pixel-wise classification resulting
in segmented output. We experimentally show the merits of OrthoSeg by
demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic
Segmentation dataset. Adaptability is one of the key motivations behind
OrthoSeg so that it serves as a useful architectural option for a wide range of
problems involving the task of semantic segmentation of coregistered multimodal
and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent
scaling of parallel encoder networks and decoder network to better match
application requirements, such as the number of input channels, the effective
field-of-view, and model capacity. | ['Kumar Shreshtha', 'Pankaj Bodani', 'Shashikant Sharma'] | 2018-11-19 | null | null | null | null | ['2d-semantic-segmentation', 'semantic-segmentation-of-orthoimagery'] | ['computer-vision', 'medical'] | [ 5.31142652e-01 2.11437464e-01 2.13052958e-01 -5.60232401e-01
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fa618ba9-cef9-4d09-add8-1591b2de2771 | analysis-of-generalized-bregman-surrogate | 2112.09191 | null | https://arxiv.org/abs/2112.09191v1 | https://arxiv.org/pdf/2112.09191v1.pdf | Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning | Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex. This paper focuses on a broad Bregman-surrogate algorithm framework including the local linear approximation, mirror descent, iterative thresholding, DC programming and many others as particular instances. The recharacterization via generalized Bregman functions enables us to construct suitable error measures and establish global convergence rates for nonconvex and nonsmooth objectives in possibly high dimensions. For sparse learning problems with a composite objective, under some regularity conditions, the obtained estimators as the surrogate's fixed points, though not necessarily local minimizers, enjoy provable statistical guarantees, and the sequence of iterates can be shown to approach the statistical truth within the desired accuracy geometrically fast. The paper also studies how to design adaptive momentum based accelerations without assuming convexity or smoothness by carefully controlling stepsize and relaxation parameters. | ['Jiuwu Jin', 'Zhifeng Wang', 'Yiyuan She'] | 2021-12-16 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-2.00159237e-01 1.04030676e-01 -3.66443157e-01 -2.30010048e-01
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5047ecd0-d2d0-4a87-864c-9137430a4321 | towards-explainable-conversational | 2305.18363 | null | https://arxiv.org/abs/2305.18363v1 | https://arxiv.org/pdf/2305.18363v1.pdf | Towards Explainable Conversational Recommender Systems | Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial. | ['Zhaochun Ren', 'Zhumin Chen', 'Pengjie Ren', 'Weiwei Sun', 'Shuo Zhang', 'Shuyu Guo'] | 2023-05-27 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [-9.20070261e-02 7.72476315e-01 -1.37906477e-01 -6.51878297e-01
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376a6313-0a46-4e06-b860-b632ace8df6e | convolutional-capsule-network-for | 1804.08376 | null | http://arxiv.org/abs/1804.08376v1 | http://arxiv.org/pdf/1804.08376v1.pdf | Convolutional capsule network for classification of breast cancer histology images | Automatization of the diagnosis of any kind of disease is of great importance
and it's gaining speed as more and more deep learning solutions are applied to
different problems. One of such computer aided systems could be a decision
support too able to accurately differentiate between different types of breast
cancer histological images - normal tissue or carcinoma. In this paper authors
present a deep learning solution, based on convolutional capsule network for
classification of four types of images of breast tissue biopsy when hematoxylin
and eusin staining is applied. The cross-validation accuracy was achieved to be
0.87 with equaly high sensitivity. | ['Tomas Iesmantas', 'Robertas Alzbutas'] | 2018-04-23 | null | null | null | null | ['classification-of-breast-cancer-histology'] | ['medical'] | [-1.80555522e-01 3.22241366e-01 -9.91124138e-02 -2.89900601e-01
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172dc7c9-eab6-4c93-8ef1-d46869c8c4bd | semi-supervised-sequence-modeling-with-cross | 1809.08370 | null | http://arxiv.org/abs/1809.08370v1 | http://arxiv.org/pdf/1809.08370v1.pdf | Semi-Supervised Sequence Modeling with Cross-View Training | Unsupervised representation learning algorithms such as word2vec and ELMo
improve the accuracy of many supervised NLP models, mainly because they can
take advantage of large amounts of unlabeled text. However, the supervised
models only learn from task-specific labeled data during the main training
phase. We therefore propose Cross-View Training (CVT), a semi-supervised
learning algorithm that improves the representations of a Bi-LSTM sentence
encoder using a mix of labeled and unlabeled data. On labeled examples,
standard supervised learning is used. On unlabeled examples, CVT teaches
auxiliary prediction modules that see restricted views of the input (e.g., only
part of a sentence) to match the predictions of the full model seeing the whole
input. Since the auxiliary modules and the full model share intermediate
representations, this in turn improves the full model. Moreover, we show that
CVT is particularly effective when combined with multi-task learning. We
evaluate CVT on five sequence tagging tasks, machine translation, and
dependency parsing, achieving state-of-the-art results. | ['Minh-Thang Luong', 'Quoc V. Le', 'Kevin Clark', 'Christopher D. Manning'] | 2018-09-22 | semi-supervised-sequence-modeling-with-cross-1 | https://aclanthology.org/D18-1217 | https://aclanthology.org/D18-1217.pdf | emnlp-2018-10 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 2.57260650e-01 6.43005610e-01 -5.91023147e-01 -6.41346514e-01
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a0f6bc63-86da-4a12-95d4-d4faf70bfc14 | heterogeneous-hand-guise-classification-based | 2101.06715 | null | https://arxiv.org/abs/2101.06715v1 | https://arxiv.org/pdf/2101.06715v1.pdf | Heterogeneous Hand Guise Classification Based on Surface Electromyographic Signals Using Multichannel Convolutional Neural Network | Electromyography (EMG) is a way of measuring the bioelectric activities that take place inside the muscles. EMG is usually performed to detect abnormalities within the nerves or muscles of a target area. The recent developments in the field of Machine Learning allow us to use EMG signals to teach machines the complex properties of human movements. Modern machines are capable of detecting numerous human activities and distinguishing among them solely based on the EMG signals produced by those activities. However, success in accomplishing this task mostly depends on the learning technique used by the machine to analyze EMG signals; and even the latest algorithms do not result in flawless classification. In this study, a novel classification method has been described employing a multichannel Convolutional Neural Network (CNN) that interprets surface EMG signals by the properties they exhibit in the power domain. The proposed method was tested on a well-established EMG dataset, and the result yields very high classification accuracy. This learning model will help researchers to develop prosthetic arms capable of detecting various hand gestures to mimic them afterwards. | ['Abdullah-Al Nahid', 'Abu Shamim Mohammad Arif', 'Niloy Sikder'] | 2021-01-17 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 6.17149591e-01 -1.61861718e-01 -3.55080009e-01 -5.04305400e-02
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f4857cb9-2d3d-4889-a12b-ec0b4f6c4504 | multithreshold-entropy-linear-classifier | 1408.1054 | null | http://arxiv.org/abs/1408.1054v1 | http://arxiv.org/pdf/1408.1054v1.pdf | Multithreshold Entropy Linear Classifier | Linear classifiers separate the data with a hyperplane. In this paper we
focus on the novel method of construction of multithreshold linear classifier,
which separates the data with multiple parallel hyperplanes. Proposed model is
based on the information theory concepts -- namely Renyi's quadratic entropy
and Cauchy-Schwarz divergence.
We begin with some general properties, including data scale invariance. Then
we prove that our method is a multithreshold large margin classifier, which
shows the analogy to the SVM, while in the same time works with much broader
class of hypotheses. What is also interesting, proposed method is aimed at the
maximization of the balanced quality measure (such as Matthew's Correlation
Coefficient) as opposed to very common maximization of the accuracy. This
feature comes directly from the optimization problem statement and is further
confirmed by the experiments on the UCI datasets.
It appears, that our Multithreshold Entropy Linear Classifier (MELC) obtaines
similar or higher scores than the ones given by SVM on both synthetic and real
data. We show how proposed approach can be benefitial for the cheminformatics
in the task of ligands activity prediction, where despite better classification
results, MELC gives some additional insight into the data structure (classes of
underrepresented chemical compunds). | ['Wojciech Marian Czarnecki', 'Jacek Tabor'] | 2014-08-04 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 1.81338027e-01 3.64215642e-01 -2.66895473e-01 -3.88027817e-01
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98505ba2-51ef-4139-a791-f6afa6b11710 | multi-task-learning-with-multi-view-attention | 1812.02354 | null | http://arxiv.org/abs/1812.02354v1 | http://arxiv.org/pdf/1812.02354v1.pdf | Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering | Answer selection and knowledge base question answering (KBQA) are two
important tasks of question answering (QA) systems. Existing methods solve
these two tasks separately, which requires large number of repetitive work and
neglects the rich correlation information between tasks. In this paper, we
tackle answer selection and KBQA tasks simultaneously via multi-task learning
(MTL), motivated by the following motivations. First, both answer selection and
KBQA can be regarded as a ranking problem, with one at text-level while the
other at knowledge-level. Second, these two tasks can benefit each other:
answer selection can incorporate the external knowledge from knowledge base
(KB), while KBQA can be improved by learning contextual information from answer
selection. To fulfill the goal of jointly learning these two tasks, we propose
a novel multi-task learning scheme that utilizes multi-view attention learned
from various perspectives to enable these tasks to interact with each other as
well as learn more comprehensive sentence representations. The experiments
conducted on several real-world datasets demonstrate the effectiveness of the
proposed method, and the performance of answer selection and KBQA is improved.
Also, the multi-view attention scheme is proved to be effective in assembling
attentive information from different representational perspectives. | ['Yang Deng', 'Yaliang Li', 'Nan Du', 'Yuexiang Xie', 'Kai Lei', 'Wei Fan', 'Min Yang', 'Ying Shen'] | 2018-12-06 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 0.0100119 -0.16805562 -0.07197787 -0.37403318 -1.1596065 -0.39147225
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4ba788d9-e998-47d1-9f09-d11ff1b17e31 | diverse-retrieval-augmented-in-context | 2307.01453 | null | https://arxiv.org/abs/2307.01453v1 | https://arxiv.org/pdf/2307.01453v1.pdf | Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking | There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for DST. First, we formulate DST as a Python programming task, explicitly modeling language coreference as variable reference in Python. Second, since in-context learning depends highly on the context examples, we propose a method to retrieve a diverse set of relevant examples to improve performance. Finally, we introduce a novel re-weighting method during decoding that takes into account probabilities of competing surface forms, and produces a more accurate dialogue state prediction. We evaluate our approach using MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero and few-shot settings. | ['Jeffrey Flanigan', 'Brendan King'] | 2023-07-04 | null | null | null | null | ['few-shot-learning', 'retrieval', 'dialogue-state-tracking'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.14285299e-01 2.51870334e-01 -3.08248162e-01 -5.74579000e-01
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5150f054-3b92-40a2-aba4-df6fef09e22c | fast-vehicle-detection-in-aerial-imagery | 1709.08666 | null | http://arxiv.org/abs/1709.08666v1 | http://arxiv.org/pdf/1709.08666v1.pdf | Fast Vehicle Detection in Aerial Imagery | In recent years, several real-time or near real-time object detectors have
been developed. However these object detectors are typically designed for
first-person view images where the subject is large in the image and do not
directly apply well to detecting vehicles in aerial imagery. Though some
detectors have been developed for aerial imagery, these are either slow or do
not handle multi-scale imagery very well. Here the popular YOLOv2 detector is
modified to vastly improve it's performance on aerial data. The modified
detector is compared to Faster RCNN on several aerial imagery datasets. The
proposed detector gives near state of the art performance at more than 4x the
speed. | ['Jennifer Carlet', 'Bernard Abayowa'] | 2017-09-25 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-1.24155045e-01 -5.08380353e-01 2.59870328e-02 -7.64701590e-02
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c277edd3-1cd5-4f6e-8f3f-2a9848639034 | finetuning-from-offline-reinforcement | 2303.17396 | null | https://arxiv.org/abs/2303.17396v1 | https://arxiv.org/pdf/2303.17396v1.pdf | Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions | Offline reinforcement learning (RL) allows for the training of competent agents from offline datasets without any interaction with the environment. Online finetuning of such offline models can further improve performance. But how should we ideally finetune agents obtained from offline RL training? While offline RL algorithms can in principle be used for finetuning, in practice, their online performance improves slowly. In contrast, we show that it is possible to use standard online off-policy algorithms for faster improvement. However, we find this approach may suffer from policy collapse, where the policy undergoes severe performance deterioration during initial online learning. We investigate the issue of policy collapse and how it relates to data diversity, algorithm choices and online replay distribution. Based on these insights, we propose a conservative policy optimization procedure that can achieve stable and sample-efficient online learning from offline pretraining. | ['Marc Peter Deisenroth', 'Edward Grefenstette', 'Jackie Kay', 'Yicheng Luo'] | 2023-03-30 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-9.73611102e-02 1.69197634e-01 -3.25658441e-01 2.43273173e-02
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095e9b38-d784-45f5-b249-c225862b9a0a | mapp-a-scalable-multi-agent-path-planning | 1401.3905 | null | http://arxiv.org/abs/1401.3905v1 | http://arxiv.org/pdf/1401.3905v1.pdf | MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees | Multi-agent path planning is a challenging problem with numerous real-life
applications. Running a centralized search such as A* in the combined state
space of all units is complete and cost-optimal, but scales poorly, as the
state space size is exponential in the number of mobile units. Traditional
decentralized approaches, such as FAR and WHCA*, are faster and more scalable,
being based on problem decomposition. However, such methods are incomplete and
provide no guarantees with respect to the running time or the solution quality.
They are not necessarily able to tell in a reasonable time whether they would
succeed in finding a solution to a given instance. We introduce MAPP, a
tractable algorithm for multi-agent path planning on undirected graphs. We
present a basic version and several extensions. They have low-polynomial
worst-case upper bounds for the running time, the memory requirements, and the
length of solutions. Even though all algorithmic versions are incomplete in the
general case, each provides formal guarantees on problems it can solve. For
each version, we discuss the algorithms completeness with respect to clearly
defined subclasses of instances. Experiments were run on realistic game grid
maps. MAPP solved 99.86% of all mobile units, which is 18--22% better than the
percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably
solvable during the first stage of plan computation. Parts of MAPPs computation
can be re-used across instances on the same map. Speed-wise, MAPP is
competitive or significantly faster than WHCA*, depending on whether MAPP
performs all computations from scratch. When data that MAPP can re-use are
preprocessed offline and readily available, MAPP is slower than the very fast
FAR algorithm by a factor of 2.18 on average. MAPPs solutions are on average
20% longer than FARs solutions and 7--31% longer than WHCA*s solutions. | ['Ko-Hsin Cindy Wang', 'Adi Botea'] | 2014-01-16 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-5.44407777e-02 5.00757337e-01 -1.76686347e-01 1.44083843e-01
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8b7de313-241c-4bf2-8d9e-c93b737de879 | distant-supervision-and-noisy-label-learning | 2003.08370 | null | https://arxiv.org/abs/2003.08370v2 | https://arxiv.org/pdf/2003.08370v2.pdf | Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá | The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countries. Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. Additionally, to alleviate some of the negative effects of the errors in automatic annotation, noise-handling methods can be integrated. Pretrained word embeddings are another key component of most neural named entity classifiers. With the advent of more complex contextual word embeddings, an interesting trade-off between model size and performance arises. While these techniques have been shown to work well in high-resource settings, we want to study how they perform in low-resource scenarios. In this work, we perform named entity recognition for Hausa and Yor\`ub\'a, two languages that are widely spoken in several developing countries. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario where it can more than double a classifier's performance. | ['David Ifeoluwa Adelani', 'Esther van den Berg', 'Michael A. Hedderich', 'Dietrich Klakow', 'Dawei Zhu'] | 2020-03-18 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.81413293e-01 9.76029187e-02 -2.02215254e-01 -5.20371914e-01
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9caeba9c-0475-48e1-9dfc-de138b56ea0c | incorporating-explicit-knowledge-in-pre | 2204.11673 | null | https://arxiv.org/abs/2204.11673v1 | https://arxiv.org/pdf/2204.11673v1.pdf | Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking | Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural language understanding. However, existing PLM based re-rankers may easily suffer from vocabulary mismatch and lack of domain specific knowledge. To alleviate these problems, explicit knowledge contained in knowledge graph is carefully introduced in our work. Specifically, we employ the existing knowledge graph which is incomplete and noisy, and first apply it in passage re-ranking task. To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage. To align both kinds of embedding in the latent space, we employ PLM as text encoder and graph neural network over knowledge meta graph as knowledge encoder. Besides, a novel knowledge injector is designed for the dynamic interaction between text and knowledge encoder. Experimental results demonstrate the effectiveness of our method especially in queries requiring in-depth domain knowledge. | ['Dawei Yin', 'Shuzi Niu', 'Zhicong Cheng', 'Shuaiqiang Wang', 'Suqi Cheng', 'Yiding Liu', 'Qian Dong'] | 2022-04-25 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-3.83397862e-02 7.34140426e-02 -5.32065868e-01 -4.22439277e-02
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8df60866-2782-419e-930c-119d87cd9627 | a-comprehensive-review-of-data-driven-co | 2301.05339 | null | https://arxiv.org/abs/2301.05339v4 | https://arxiv.org/pdf/2301.05339v4.pdf | A Comprehensive Review of Data-Driven Co-Speech Gesture Generation | Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development. | ['Michael Neff', 'Gustav Eje Henter', 'Chaitanya Ahuja', 'Taras Kucherenko', 'Simbarashe Nyatsanga'] | 2023-01-13 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 1.71506509e-01 4.05592695e-02 3.68480035e-03 -9.96422097e-02
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8e539bc7-a73a-447e-b630-ab738eb160bd | reverse-survival-model-rsm-a-pipeline-for | 2210.15674 | null | https://arxiv.org/abs/2210.15674v1 | https://arxiv.org/pdf/2210.15674v1.pdf | Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models | The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications. Ensuring the reliability and explainability of deep survival models deployed in healthcare is a necessity. Since DNN models often behave like a black box, their predictions might not be easily trusted by clinicians, especially when predictions are contrary to a physician's opinion. A deep survival model that explains and justifies its decision-making process could potentially gain the trust of clinicians. In this research, we propose the reverse survival model (RSM) framework that provides detailed insights into the decision-making process of survival models. For each patient of interest, RSM can extract similar patients from a dataset and rank them based on the most relevant features that deep survival models rely on for their predictions. | ['Nick Sajadi', 'Mansour Abolghasemian', 'Mohammad Alavinia', 'Mohammad Shafiee', 'Amir Sameizadeh', 'Navid Ziaei', 'Ebrahim Pourjafari', 'Reza Saadati Fard', 'Mohammad R. Rezaei'] | 2022-10-27 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.93566903e-01 1.59196332e-01 -1.90843135e-01 -7.30353892e-01
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36c3f2f0-c553-4ebb-b93b-23e349bc6e6a | table-detection-in-the-wild-a-novel-diverse-1 | 2209.09207 | null | https://arxiv.org/abs/2209.09207v1 | https://arxiv.org/pdf/2209.09207v1.pdf | Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method | Recent deep learning approaches in table detection achieved outstanding performance and proved to be effective in identifying document layouts. Currently, available table detection benchmarks have many limitations, including the lack of samples diversity, simple table structure, the lack of training cases, and samples quality. In this paper, we introduce a diverse large-scale dataset for table detection with more than seven thousand samples containing a wide variety of table structures collected from many diverse sources. In addition to that, we also present baseline results using a convolutional neural network-based method to detect table structure in documents. Experimental results show the superiority of applying convolutional deep learning methods over classical computer vision-based methods. The introduction of this diverse table detection dataset will enable the community to develop high throughput deep learning methods for understanding document layout and tabular data processing. | ['Sanjay G', 'Siddhant Swaroop Dash', 'Nikhil Fande', 'Shashank Shekhar', 'Mrinal Haloi'] | 2022-08-31 | table-detection-in-the-wild-a-novel-diverse | https://arxiv.org/abs/2209.09207 | https://arxiv.org/pdf/2209.09207 | null | ['table-detection'] | ['miscellaneous'] | [-2.80174166e-02 -4.08563793e-01 -2.79782206e-01 -3.36582482e-01
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171c5933-f19e-44b4-a6db-36f43219425b | heterogeneous-federated-knowledge-graph | 2302.02069 | null | https://arxiv.org/abs/2302.02069v2 | https://arxiv.org/pdf/2302.02069v2.pdf | Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning | Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting. | ['Wei Hu', 'Guangyao Li', 'Xiangrong Zhu'] | 2023-02-04 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-4.30260867e-01 2.02634886e-01 -4.14485812e-01 -7.28549138e-02
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7b4bd3ae-a41c-4f99-a254-43e06f208a97 | improving-opinion-spam-detection-by | 2012.13905 | null | https://arxiv.org/abs/2012.13905v1 | https://arxiv.org/pdf/2012.13905v1.pdf | Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution | Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them, by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers. | ['Marinella Petrocchi', 'Gianluca Lax', 'Francesco Buccafurri', 'Michela Fazzolari'] | 2020-12-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.34884208e-01 4.44816090e-02 -2.27478772e-01 -4.68110502e-01
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f9fcdb26-4a82-49db-8c01-6882cb5efd3d | blind-image-deconvolution-using-pretrained | 1908.07404 | null | https://arxiv.org/abs/1908.07404v1 | https://arxiv.org/pdf/1908.07404v1.pdf | Blind Image Deconvolution using Pretrained Generative Priors | This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks. We employ two separate deep generative models - one trained to produce sharp images while the other trained to generate blur kernels from lower dimensional parameters. To deblur, we propose an alternating gradient descent scheme operating in the latent lower-dimensional space of each of the pretrained generative models. Our experiments show excellent deblurring results even under large blurs and heavy noise. To improve the performance on rich image datasets not well learned by the generative networks, we present a modification of the proposed scheme that governs the deblurring process under both generative and classical priors. | ['Muhammad Asim', 'Fahad Shamshad', 'Ali Ahmed'] | 2019-08-20 | null | null | null | null | ['blind-image-deblurring', 'image-deconvolution'] | ['computer-vision', 'computer-vision'] | [ 1.92614466e-01 -1.76542103e-01 5.17749429e-01 -1.29141912e-01
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52085377-75c6-4804-b7d7-ae96de5b6092 | learning-to-drop-points-for-lidar-scan | 2102.11952 | null | https://arxiv.org/abs/2102.11952v2 | https://arxiv.org/pdf/2102.11952v2.pdf | Learning to Drop Points for LiDAR Scan Synthesis | 3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on. Generative modeling of LiDAR data as scene priors is one of the promising solutions to compensate for unreliable or incomplete observations. In this paper, we propose a novel generative model for learning LiDAR data based on generative adversarial networks. As in the related studies, we process LiDAR data as a compact yet lossless representation, a cylindrical depth map. However, despite the smoothness of real-world objects, many points on the depth map are dropped out through the laser measurement, which causes learning difficulty on generative models. To circumvent this issue, we introduce measurement uncertainty into the generation process, which allows the model to learn a disentangled representation of the underlying shape and the dropout noises from a collection of real LiDAR data. To simulate the lossy measurement, we adopt a differentiable sampling framework to drop points based on the learned uncertainty. We demonstrate the effectiveness of our method on synthesis and reconstruction tasks using two datasets. We further showcase potential applications by restoring LiDAR data with various types of corruption. | ['Ryo Kurazume', 'Kazuto Nakashima'] | 2021-02-23 | null | null | null | null | ['sensor-modeling', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 3.34299862e-01 2.32788950e-01 1.83682665e-01 -4.31233883e-01
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f4292095-97fa-46b5-88b3-de9a879ccf69 | surgicalgpt-end-to-end-language-vision-gpt | 2304.09974 | null | https://arxiv.org/abs/2304.09974v1 | https://arxiv.org/pdf/2304.09974v1.pdf | SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery | Advances in GPT-based large language models (LLMs) are revolutionizing natural language processing, exponentially increasing its use across various domains. Incorporating uni-directional attention, these autoregressive LLMs can generate long and coherent paragraphs. However, for visual question answering (VQA) tasks that require both vision and language processing, models with bi-directional attention or models employing fusion techniques are often employed to capture the context of multiple modalities all at once. As GPT does not natively process vision tokens, to exploit the advancements in GPT models for VQA in robotic surgery, we design an end-to-end trainable Language-Vision GPT (LV-GPT) model that expands the GPT2 model to include vision input (image). The proposed LV-GPT incorporates a feature extractor (vision tokenizer) and vision token embedding (token type and pose). Given the limitations of unidirectional attention in GPT models and their ability to generate coherent long paragraphs, we carefully sequence the word tokens before vision tokens, mimicking the human thought process of understanding the question to infer an answer from an image. Quantitatively, we prove that the LV-GPT model outperforms other state-of-the-art VQA models on two publically available surgical-VQA datasets (based on endoscopic vision challenge robotic scene segmentation 2018 and CholecTriplet2021) and on our newly annotated dataset (based on the holistic surgical scene dataset). We further annotate all three datasets to include question-type annotations to allow sub-type analysis. Furthermore, we extensively study and present the effects of token sequencing, token type and pose embedding for vision tokens in the LV-GPT model. | ['Hongliang Ren', 'Gokul Kannan', 'Mobarakol Islam', 'Lalithkumar Seenivasan'] | 2023-04-19 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.89195210e-01 6.35232866e-01 -1.12410270e-01 -1.40217364e-01
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5bfb0e46-79bf-443a-b75b-30f37b53c1f4 | a-visuospatial-dataset-for-naturalistic-verb | 2010.15225 | null | https://arxiv.org/abs/2010.15225v1 | https://arxiv.org/pdf/2010.15225v1.pdf | A Visuospatial Dataset for Naturalistic Verb Learning | We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access: That is, naturalistic, spontaneous speech paired with richly grounded visuospatial context. We use the collected data to compare several distributional semantics models for verb learning. We evaluate neural models based on 2D (pixel) features as well as feature-engineered models based on 3D (symbolic, spatial) features, and show that neither modeling approach achieves satisfactory performance. Our results are consistent with evidence from child language acquisition that emphasizes the difficulty of learning verbs from naive distributional data. We discuss avenues for future work on cognitively-inspired grounded language learning, and release our corpus with the intent of facilitating research on the topic. | ['Ellie Pavlick', 'Dylan Ebert'] | 2020-10-28 | null | https://aclanthology.org/2020.starsem-1.16 | https://aclanthology.org/2020.starsem-1.16.pdf | joint-conference-on-lexical-and-computational | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.82041109e-01 3.93789530e-01 -2.13723592e-02 -6.66157126e-01
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a24ded9e-1dea-472b-ad52-594d17f289f5 | view-gcn-view-based-graph-convolutional | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wei_View-GCN_View-Based_Graph_Convolutional_Network_for_3D_Shape_Analysis_CVPR_2020_paper.pdf | View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis | View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view features to be a global shape descriptor. In this work, we propose a novel view-based Graph Convolutional Neural Network, dubbed as view-GCN, to recognize 3D shape based on graph representation of multiple views in flexible view configurations. We first construct view-graph with multiple views as graph nodes, then design a graph convolutional neural network over view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. The view-GCN is a hierarchical network based on local and non-local graph convolution for feature transform, and selective view-sampling for graph coarsening. Extensive experiments on benchmark datasets show that view-GCN achieves state-of-the-art results for 3D shape classification and retrieval.
| [' Jian Sun', ' Ruixuan Yu', 'Xin Wei'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-3.64501595e-01 -3.91266942e-01 -8.37660655e-02 -5.94042599e-01
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0db00088-57d7-4c30-a4e9-eb4d2a927149 | language-agnostic-twitter-bot-detection | null | null | https://aclanthology.org/R19-1065 | https://aclanthology.org/R19-1065.pdf | Language-Agnostic Twitter-Bot Detection | In this paper we address the problem of detecting Twitter bots. We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots. We use this data to train machine learning classifiers that distinguish between real and bot accounts. We identify features that are easy to extract while still providing good results. We analyze different feature groups based on account specific, tweet specific and behavioral specific features and measure their performance compared to other state of the art bot detection methods. For easy future portability of our work we focus on language-agnostic features. With AdaBoost, the best performing classifier, we achieve an accuracy of 0.988 and an AUC of 0.995. As the creation of good training data in machine learning is often difficult - especially in the domain of Twitter bot detection - we additionally analyze to what extent smaller amounts of training data lead to useful results by reviewing cross-validated learning curves. Our results indicate that using few but expressive features already has a good practical benefit for bot detection, especially if only a small amount of training data is available. | ['J{\\"u}rgen Knauth'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.16489270e-01 -1.82924286e-01 -2.14198768e-01 2.24871691e-02
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06f30453-c25e-4274-8d83-56deae85d12b | using-mise-en-scene-visual-features-based-on | 1704.06109 | null | http://arxiv.org/abs/1704.06109v1 | http://arxiv.org/pdf/1704.06109v1.pdf | Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation | Item features play an important role in movie recommender systems, where
recommendations can be generated by using explicit or implicit preferences of
users on traditional features (attributes) such as tag, genre, and cast.
Typically, movie features are human-generated, either editorially (e.g., genre
and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such,
they are prone to noise and are expensive to collect. Moreover, these features
are often rare or absent for new items, making it difficult or even impossible
to provide good quality recommendations.
In this paper, we show that user's preferences on movies can be better
described in terms of the mise-en-sc\`ene features, i.e., the visual aspects of
a movie that characterize design, aesthetics and style (e.g., colors,
textures). We use both MPEG-7 visual descriptors and Deep Learning hidden
layers as example of mise-en-sc\`ene features that can visually describe
movies. Interestingly, mise-en-sc\`ene features can be computed automatically
from video files or even from trailers, offering more flexibility in handling
new items, avoiding the need for costly and error-prone human-based tagging,
and providing good scalability.
We have conducted a set of experiments on a large catalogue of 4K movies.
Results show that recommendations based on mise-en-sc\`ene features
consistently provide the best performance with respect to richer sets of more
traditional features, such as genre and tag. | ['Paolo Cremonesi', 'Massimo Quadrana', 'Yashar Deldjoo', 'Mehdi Elahi'] | 2017-04-20 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.17062861e-01 -4.57540900e-01 -6.21163175e-02 -5.24306655e-01
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919d6477-e724-4351-bf41-7a16f8a9a3d5 | robustness-testing-of-ai-systems-a-case-study | 2108.06159 | null | https://arxiv.org/abs/2108.06159v1 | https://arxiv.org/pdf/2108.06159v1.pdf | Robustness testing of AI systems: A case study for traffic sign recognition | In the last years, AI systems, in particular neural networks, have seen a tremendous increase in performance, and they are now used in a broad range of applications. Unlike classical symbolic AI systems, neural networks are trained using large data sets and their inner structure containing possibly billions of parameters does not lend itself to human interpretation. As a consequence, it is so far not feasible to provide broad guarantees for the correct behaviour of neural networks during operation if they process input data that significantly differ from those seen during training. However, many applications of AI systems are security- or safety-critical, and hence require obtaining statements on the robustness of the systems when facing unexpected events, whether they occur naturally or are induced by an attacker in a targeted way. As a step towards developing robust AI systems for such applications, this paper presents how the robustness of AI systems can be practically examined and which methods and metrics can be used to do so. The robustness testing methodology is described and analysed for the example use case of traffic sign recognition in autonomous driving. | ['Arndt von Twickel', 'Petar Tsankov', 'Matthias Neu', 'Pavol Bielik', 'Christian Berghoff'] | 2021-08-13 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 6.40331566e-01 2.24075139e-01 1.06314577e-01 -4.80703890e-01
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19e25b63-141b-44e0-827b-b22b0c0315e7 | recognize-anything-a-strong-image-tagging | 2306.03514 | null | https://arxiv.org/abs/2306.03514v3 | https://arxiv.org/pdf/2306.03514v3.pdf | Recognize Anything: A Strong Image Tagging Model | We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google tagging API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision. | ['Lei Zhang', 'Yandong Guo', 'Shilong Liu', 'Yaqian Li', 'Tong Luo', 'Yuzhuo Qin', 'Yanchun Xie', 'Zhaochuan Luo', 'Zhaoyang Li', 'Jinyu Ma', 'Xinyu Huang', 'Youcai Zhang'] | 2023-06-06 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.08806598e-01 2.14443162e-01 -2.69121170e-01 -4.82743502e-01
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53ed3b03-1f17-4209-aa0e-c29b2b4d440b | babe-enhancing-fairness-via-estimation-of | 2307.02891 | null | https://arxiv.org/abs/2307.02891v1 | https://arxiv.org/pdf/2307.02891v1.pdf | BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables | We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead to bad accuracy and do not really achieve fairness in situations where there is a correlation between the sensitive attribute S and the legitimate attribute E (explanatory variable) that should determine the decision. To overcome these drawbacks, other notions of fairness have been proposed, in particular, conditional statistical parity and equal opportunity. However, E is often not directly observable in the data, i.e., it is a latent variable. We may observe some other variable Z representing E, but the problem is that Z may also be affected by S, hence Z itself can be biased. To deal with this problem, we propose BaBE (Bayesian Bias Elimination), an approach based on a combination of Bayes inference and the Expectation-Maximization method, to estimate the most likely value of E for a given Z for each group. The decision can then be based directly on the estimated E. We show, by experiments on synthetic and real data sets, that our approach provides a good level of fairness as well as high accuracy. | ['Catuscia Palamidessi', 'Daniele Gorla', 'Ruta Binkyte'] | 2023-07-06 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 2.90205687e-01 2.88931906e-01 -3.47584456e-01 -8.84577334e-01
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31ce3021-cacb-4e0e-815b-e023d1d37382 | decision-making-under-uncertainty-a-game-of | 2012.07509 | null | https://arxiv.org/abs/2012.07509v1 | https://arxiv.org/pdf/2012.07509v1.pdf | Decision Making under Uncertainty: A Game of Two Selves | In this paper we characterize the niveloidal preferences that satisfy the Weak Order, Monotonicity, Archimedean, and Weak C-Independence Axioms from the point of view of an intra-personal, leader-follower game. We also show that the leader's strategy space can serve as an ambiguity aversion index. | ['Jianming Xia'] | 2020-12-14 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-3.06708246e-01 2.64241189e-01 -3.42971683e-01 -6.03058875e-01
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2f19b3f5-dc8b-4fa1-b9ac-d748c97d498a | deep-discriminative-spatial-and-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pan_Deep_Discriminative_Spatial_and_Temporal_Network_for_Efficient_Video_Deblurring_CVPR_2023_paper.pdf | Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring | How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity. | ['Jinhui Tang', 'Jianjun Ge', 'Jiangxin Dong', 'Boming Xu', 'Jinshan Pan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.90778807e-01 -8.87055039e-01 -2.97792137e-01 -1.12706810e-01
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916d3819-38ca-40d4-b1bf-aa39d3544143 | simple-open-vocabulary-object-detection-with | 2205.06230 | null | https://arxiv.org/abs/2205.06230v2 | https://arxiv.org/pdf/2205.06230v2.pdf | Simple Open-Vocabulary Object Detection with Vision Transformers | Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub. | ['Neil Houlsby', 'Thomas Kipf', 'Xiaohua Zhai', 'Xiao Wang', 'Zhuoran Shen', 'Mostafa Dehghani', 'Anurag Arnab', 'Aravindh Mahendran', 'Alexey Dosovitskiy', 'Dirk Weissenborn', 'Maxim Neumann', 'Austin Stone', 'Alexey Gritsenko', 'Matthias Minderer'] | 2022-05-12 | null | null | null | null | ['one-shot-object-detection', 'open-vocabulary-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.32157099e-01 -2.46536776e-01 -5.26104979e-02 -5.12906253e-01
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48f5ec90-f9f4-4049-b2a0-49f9d84e19b4 | novel-class-discovery-an-introduction-and-key | 2302.12028 | null | https://arxiv.org/abs/2302.12028v1 | https://arxiv.org/pdf/2302.12028v1.pdf | Novel Class Discovery: an Introduction and Key Concepts | Novel Class Discovery (NCD) is a growing field where we are given during training a labeled set of known classes and an unlabeled set of different classes that must be discovered. In recent years, many methods have been proposed to address this problem, and the field has begun to mature. In this paper, we provide a comprehensive survey of the state-of-the-art NCD methods. We start by formally defining the NCD problem and introducing important notions. We then give an overview of the different families of approaches, organized by the way they transfer knowledge from the labeled set to the unlabeled set. We find that they either learn in two stages, by first extracting knowledge from the labeled data only and then applying it to the unlabeled data, or in one stage by conjointly learning on both sets. For each family, we describe their general principle and detail a few representative methods. Then, we briefly introduce some new related tasks inspired by the increasing number of NCD works. We also present some common tools and techniques used in NCD, such as pseudo labeling, self-supervised learning and contrastive learning. Finally, to help readers unfamiliar with the NCD problem differentiate it from other closely related domains, we summarize some of the closest areas of research and discuss their main differences. | ['Sandrine Vaton', 'Joachim Flocon-Cholet', 'Alexandre Reiffers-Masson', 'Stéphane Gosselin', 'Vincent Lemaire', 'Colin Troisemaine'] | 2023-02-22 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.87038255e-01 7.07052350e-02 -7.08441079e-01 -5.80457926e-01
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f7a31a05-93ee-4769-88d9-80c6caa5bedf | impact-of-different-desired-velocity-profiles | 2306.01971 | null | https://arxiv.org/abs/2306.01971v1 | https://arxiv.org/pdf/2306.01971v1.pdf | Impact of Different Desired Velocity Profiles and Controller Gains on Convoy Driveability of Cooperative Adaptive Cruise Control Operated Platoons | As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets. | ['Levent Guvenc', 'Santhosh Tamilarasan'] | 2023-06-03 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-3.15703630e-01 3.40101659e-01 -2.76879430e-01 -5.16836233e-02
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c224d428-4639-40e1-8c9a-ae2f7d85eec9 | unet-2022-exploring-dynamics-in-non | 2210.15566 | null | https://arxiv.org/abs/2210.15566v1 | https://arxiv.org/pdf/2210.15566v1.pdf | UNet-2022: Exploring Dynamics in Non-isomorphic Architecture | Recent medical image segmentation models are mostly hybrid, which integrate self-attention and convolution layers into the non-isomorphic architecture. However, one potential drawback of these approaches is that they failed to provide an intuitive explanation of why this hybrid combination manner is beneficial, making it difficult for subsequent work to make improvements on top of them. To address this issue, we first analyze the differences between the weight allocation mechanisms of the self-attention and convolution. Based on this analysis, we propose to construct a parallel non-isomorphic block that takes the advantages of self-attention and convolution with simple parallelization. We name the resulting U-shape segmentation model as UNet-2022. In experiments, UNet-2022 obviously outperforms its counterparts in a range segmentation tasks, including abdominal multi-organ segmentation, automatic cardiac diagnosis, neural structures segmentation, and skin lesion segmentation, sometimes surpassing the best performing baseline by 4%. Specifically, UNet-2022 surpasses nnUNet, the most recognized segmentation model at present, by large margins. These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation. | ['Yizhou Yu', 'Liansheng Wang', 'Hong-Yu Zhou', 'Jiansen Guo'] | 2022-10-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.20401970e-02 4.03039426e-01 -1.05794132e-01 -2.69020766e-01
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dfa9d303-1221-4e22-a240-e2ba0a46683e | vae-based-text-style-transfer-with-pivot | 2112.03154 | null | https://arxiv.org/abs/2112.03154v1 | https://arxiv.org/pdf/2112.03154v1.pdf | VAE based Text Style Transfer with Pivot Words Enhancement Learning | Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parallel training data, unsupervised learning has become a trending direction for TST tasks. In this paper, we propose a novel VAE based Text Style Transfer with pivOt Words Enhancement leaRning (VT-STOWER) method which utilizes Variational AutoEncoder (VAE) and external style embeddings to learn semantics and style distribution jointly. Additionally, we introduce pivot words learning, which is applied to learn decisive words for a specific style and thereby further improve the overall performance of the style transfer. The proposed VT-STOWER can be scaled to different TST scenarios given very limited and non-parallel training data with a novel and flexible style strength control mechanism. Experiments demonstrate that the VT-STOWER outperforms the state-of-the-art on sentiment, formality, and code-switching TST tasks. | ['Chenlei Guo', 'Chengyuan Ma', 'Zhongkai Sun', 'Sixing Lu', 'Haoran Xu'] | 2021-12-06 | null | https://aclanthology.org/2021.icon-main.20 | https://aclanthology.org/2021.icon-main.20.pdf | icon-2021-12 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.59145498e-01 -2.66457051e-01 6.47359993e-03 -5.62629640e-01
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d7dfad0f-711f-4755-b49f-f84524a9a080 | improving-and-diagnosing-knowledge-based | 2112.06888 | null | https://arxiv.org/abs/2112.06888v1 | https://arxiv.org/pdf/2112.06888v1.pdf | Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection | Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent single modality text work has shown knowledge injection into pre-trained language models, specifically entity enhanced knowledge graph embeddings, can improve performance on downstream entity-centric tasks. In this work, we empirically study how and whether such methods, applied in a bi-modal setting, can improve an existing VQA system's performance on the KBVQA task. We experiment with two large publicly available VQA datasets, (1) KVQA which contains mostly rare Wikipedia entities and (2) OKVQA which is less entity-centric and more aligned with common sense reasoning. Both lack explicit entity spans and we study the effect of different weakly supervised and manual methods for obtaining them. Additionally we analyze how recently proposed bi-modal and single modal attention explanations are affected by the incorporation of such entity enhanced representations. Our results show substantial improved performance on the KBVQA task without the need for additional costly pre-training and we provide insights for when entity knowledge injection helps improve a model's understanding. We provide code and enhanced datasets for reproducibility. | ['Joydeep Ghosh', 'Yasumasa Onoe', 'Diego Garcia-Olano'] | 2021-12-13 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.69688724e-02 5.22902429e-01 -9.51644480e-02 -2.58384109e-01
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177cb64d-5335-48af-bf0e-3062a2e68f1a | an-energy-based-prior-for-generative-saliency | 2204.08803 | null | https://arxiv.org/abs/2204.08803v3 | https://arxiv.org/pdf/2204.08803v3.pdf | An Energy-Based Prior for Generative Saliency | We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Both the parameters of saliency generator and the energy-based prior are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Different from existing generative models, which define the prior distribution of the latent variables as a simple isotropic Gaussian distribution, our model uses an energy-based informative prior which can be more expressive in capturing the latent space of the data. With the informative energy-based prior, we extend the Gaussian distribution assumption of generative models to achieve a more representative distribution of the latent space, leading to more reliable uncertainty estimation. We apply the proposed frameworks to both RGB and RGB-D salient object detection tasks with both transformer and convolutional neural network backbones. We further propose an adversarial learning algorithm and a variational inference algorithm as alternatives to train the proposed generative framework. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps that are consistent with human perception. Results and code are available at \url{https://github.com/JingZhang617/EBMGSOD}. | ['Ping Li', 'Nick Barnes', 'Jianwen Xie', 'Jing Zhang'] | 2022-04-19 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.34468430e-01 3.26658070e-01 -3.70196626e-02 -2.45376498e-01
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195e6607-624b-46dd-8e1e-5f1514693e2f | ncis-deep-color-gradient-maps-regression-and | 2306.15784 | null | https://arxiv.org/abs/2306.15784v1 | https://arxiv.org/pdf/2306.15784v1.pdf | NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images | Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells. | ['Enrico Grisan', 'Livio Corain', 'Livio Finos', 'Jean-Marie Graïc', 'Antonella Peruffo', 'Valentina Vadori'] | 2023-06-27 | null | null | null | null | ['instance-segmentation'] | ['computer-vision'] | [ 2.54097313e-01 2.96675950e-01 2.59836137e-01 -3.72282058e-01
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1d9179b9-ee26-4f46-968b-acebfee2e04e | topological-data-analysis-for-arrhythmia | 1906.05795 | null | https://arxiv.org/abs/1906.05795v1 | https://arxiv.org/pdf/1906.05795v1.pdf | Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks | This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that turns out to be effective for new unseen patient. The novelty of our approach relies on the use of topological data analysis as basis of our multichannel architecture, to diminish the bias due to individual differences. We show that our structure reaches the performances of the state-of-the-art methods regarding arrhythmia detection and classification. | ['Meryll Dindin', 'Frederic Chazal', 'Yuhei Umeda'] | 2019-06-13 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-1.98907722e-02 1.35614052e-01 4.78811413e-01 -7.31017888e-02
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b692bfaf-3a5e-4a99-96a8-d59b93932211 | a-mathematical-framework-for-learning | 2212.11481 | null | https://arxiv.org/abs/2212.11481v2 | https://arxiv.org/pdf/2212.11481v2.pdf | A Mathematical Framework for Learning Probability Distributions | The modeling of probability distributions, specifically generative modeling and density estimation, has become an immensely popular subject in recent years by virtue of its outstanding performance on sophisticated data such as images and texts. Nevertheless, a theoretical understanding of its success is still incomplete. One mystery is the paradox between memorization and generalization: In theory, the model is trained to be exactly the same as the empirical distribution of the finite samples, whereas in practice, the trained model can generate new samples or estimate the likelihood of unseen samples. Likewise, the overwhelming diversity of distribution learning models calls for a unified perspective on this subject. This paper provides a mathematical framework such that all the well-known models can be derived based on simple principles. To demonstrate its efficacy, we present a survey of our results on the approximation error, training error and generalization error of these models, which can all be established based on this framework. In particular, the aforementioned paradox is resolved by proving that these models enjoy implicit regularization during training, so that the generalization error at early-stopping avoids the curse of dimensionality. Furthermore, we provide some new results on landscape analysis and the mode collapse phenomenon. | ['Hongkang Yang'] | 2022-12-22 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.56452417e-01 1.07158177e-01 -1.93568617e-01 -1.29255086e-01
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4a9f5ad8-34f3-44ad-b3d1-ead98d89e346 | robust-model-based-3d-head-pose-estimation | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Meyer_Robust_Model-Based_3D_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Meyer_Robust_Model-Based_3D_ICCV_2015_paper.pdf | Robust Model-Based 3D Head Pose Estimation | We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term. The pose is estimated on the fly without requiring an explicit initialization or training phase. Our method handles large pose angles and partial occlusions by dynamically adapting to the reliable visible parts of the face. It is robust and generalizes to different depth sensors without modification. On the Biwi Kinect dataset, we achieve best-in-class performance, with average angular errors of 2.1, 2.1 and 2.4 degrees for yaw, pitch, and roll, respectively, and an average translational error of 5.9 mm, while running at 6 fps on a graphics processing unit. | ['Dikpal Reddy', 'Jan Kautz', 'Iuri Frosio', 'Gregory P. Meyer', 'Shalini Gupta'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['head-pose-estimation'] | ['computer-vision'] | [ 5.70427924e-02 3.67715836e-01 1.49978563e-01 -4.85386848e-01
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8f8953f2-a429-41a6-845e-3ee4567ce864 | exploring-looping-effects-in-rnn-based | null | null | https://aclanthology.org/2020.alta-1.15 | https://aclanthology.org/2020.alta-1.15.pdf | Exploring Looping Effects in RNN-based Architectures | The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of morphological reinflection task as a proxy to study the effects of the changes. Our results show that the probability of the occurrence of repetitive loops is significantly reduced by introduction of an extra neural decoder output. The output should be specifically trained to produce gradually increasing value upon generation of each character of a given sequence. We also explored variations of the technique and found that feeding the extra output back to the decoder amplifies the positive effects. | ['Ekaterina Vylomova', 'Saliha Muradoglu', 'Andrei Shcherbakov'] | null | null | null | null | alta-2020-12 | ['morphological-inflection'] | ['natural-language-processing'] | [ 8.67929101e-01 5.69257617e-01 -2.35319734e-02 2.06463598e-02
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a9df3c25-97da-4498-aeda-76fa356b474e | zscribbleseg-zen-and-the-art-of-scribble | 2301.04882 | null | https://arxiv.org/abs/2301.04882v1 | https://arxiv.org/pdf/2301.04882v1.pdf | ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation | Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images. To alleviate this problem, we propose to utilize solely scribble annotations for weakly supervised segmentation. Existing solutions mainly leverage selective losses computed solely on annotated areas and generate pseudo gold standard segmentation by propagating labels to adjacent areas. However, these methods could suffer from the inaccurate and sometimes unrealistic pseudo segmentation due to the insufficient supervision and incomplete shape features. Different from previous efforts, we first investigate the principle of ''good scribble annotations'', which leads to efficient scribble forms via supervision maximization and randomness simulation. Furthermore, we introduce regularization terms to encode the spatial relationship and shape prior, where a new formulation is developed to estimate the mixture ratios of label classes. These ratios are critical in identifying the unlabeled pixels for each class and correcting erroneous predictions, thus the accurate estimation lays the foundation for the incorporation of spatial prior. Finally, we integrate the efficient scribble supervision with the prior into a unified framework, denoted as ZScribbleSeg, and apply the method to multiple scenarios. Leveraging only scribble annotations, ZScribbleSeg set new state-of-the-arts on four segmentation tasks using ACDC, MSCMRseg, MyoPS and PPSS datasets. | ['Xiahai Zhuang', 'Ke Zhang'] | 2023-01-12 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 4.24134791e-01 3.96199226e-01 -1.44196823e-01 -4.66631085e-01
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ba446b5e-09d8-418d-a907-4e28529894d3 | revisiting-the-shape-bias-of-deep-learning | 2206.06466 | null | https://arxiv.org/abs/2206.06466v1 | https://arxiv.org/pdf/2206.06466v1.pdf | Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification | It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of shape-biases for the classification of skin lesion images. Our analysis shows that different skin lesion datasets exhibit varying biases towards individual image features. Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation. This indicates that these features are still represented in the learnt embedding spaces of the models, but not used for classification. In addition, the spectral analysis of different datasets shows that in contrast to common visual recognition, dermoscopic skin lesion classification, by nature, is reliant on complex feature combinations beyond shape-bias. As a natural consequence, shifting away from the prevalent desire of shape-biasing models can even improve skin lesion classifiers in some cases. | ['Sheraz Ahmed', 'Andreas Dengel', 'Shoaib Ahmed Siddiqui', 'Christoph Peter Balada', 'Fabian Schmeisser', 'Adriano Lucieri'] | 2022-06-13 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.16994441e-01 4.11667824e-01 -1.19424373e-01 -4.02183682e-01
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db110fd8-55c0-4dff-b5fa-36fb6df7f400 | broad-coverage-semantic-parsing-as | 1909.02607 | null | https://arxiv.org/abs/1909.02607v2 | https://arxiv.org/pdf/1909.02607v2.pdf | Broad-Coverage Semantic Parsing as Transduction | We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the transducer can be effectively trained without relying on a pre-trained aligner. Experiments conducted on three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA -- demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP. | ['Kevin Duh', 'Xutai Ma', 'Benjamin Van Durme', 'Sheng Zhang'] | 2019-09-05 | broad-coverage-semantic-parsing-as-1 | https://aclanthology.org/D19-1392 | https://aclanthology.org/D19-1392.pdf | ijcnlp-2019-11 | ['ucca-parsing'] | ['natural-language-processing'] | [ 8.06436181e-01 5.97752035e-01 -2.40029350e-01 -7.08581924e-01
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438a1012-a257-498c-97d1-43d7d45af134 | chateval-a-tool-for-chatbot-evaluation | null | null | https://aclanthology.org/N19-4011 | https://aclanthology.org/N19-4011.pdf | ChatEval: A Tool for Chatbot Evaluation | Open-domain dialog systems (i.e. chatbots) are difficult to evaluate. The current best practice for analyzing and comparing these dialog systems is the use of human judgments. However, the lack of standardization in evaluation procedures, and the fact that model parameters and code are rarely published hinder systematic human evaluation experiments. We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems. Researchers can submit their trained models to the ChatEval web interface and obtain comparisons with baselines and prior work. The evaluation code is open-source to ensure standardization and transparency. In addition, we introduce open-source baseline models and evaluation datasets. ChatEval can be found at https://chateval.org. | ['Chris Callison-Burch', 'Arun Kirubarajan', 'Jo{\\~a}o Sedoc', 'Daphne Ippolito', 'Jai Thirani', 'Lyle Ungar'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['open-domain-dialog'] | ['natural-language-processing'] | [-5.74530661e-01 -2.69565061e-02 -1.13877237e-01 -7.15831339e-01
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23ce8435-ad2c-491b-98e5-41291932a58b | real-world-anomaly-detection-in-surveillance | 1801.04264 | null | http://arxiv.org/abs/1801.04264v3 | http://arxiv.org/pdf/1801.04264v3.pdf | Real-world Anomaly Detection in Surveillance Videos | Surveillance videos are able to capture a variety of realistic anomalies. In
this paper, we propose to learn anomalies by exploiting both normal and
anomalous videos. To avoid annotating the anomalous segments or clips in
training videos, which is very time consuming, we propose to learn anomaly
through the deep multiple instance ranking framework by leveraging weakly
labeled training videos, i.e. the training labels (anomalous or normal) are at
video-level instead of clip-level. In our approach, we consider normal and
anomalous videos as bags and video segments as instances in multiple instance
learning (MIL), and automatically learn a deep anomaly ranking model that
predicts high anomaly scores for anomalous video segments. Furthermore, we
introduce sparsity and temporal smoothness constraints in the ranking loss
function to better localize anomaly during training. We also introduce a new
large-scale first of its kind dataset of 128 hours of videos. It consists of
1900 long and untrimmed real-world surveillance videos, with 13 realistic
anomalies such as fighting, road accident, burglary, robbery, etc. as well as
normal activities. This dataset can be used for two tasks. First, general
anomaly detection considering all anomalies in one group and all normal
activities in another group. Second, for recognizing each of 13 anomalous
activities. Our experimental results show that our MIL method for anomaly
detection achieves significant improvement on anomaly detection performance as
compared to the state-of-the-art approaches. We provide the results of several
recent deep learning baselines on anomalous activity recognition. The low
recognition performance of these baselines reveals that our dataset is very
challenging and opens more opportunities for future work. The dataset is
available at: https://webpages.uncc.edu/cchen62/dataset.html | ['Chen Chen', 'Waqas Sultani', 'Mubarak Shah'] | 2018-01-12 | real-world-anomaly-detection-in-surveillance-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sultani_Real-World_Anomaly_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sultani_Real-World_Anomaly_Detection_CVPR_2018_paper.pdf | cvpr-2018-6 | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.72030622e-01 -3.52597177e-01 -1.40128523e-01 -3.73167008e-01
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5bbaa1e0-4d38-47c9-9a08-ae9c9fb28b46 | proceedings-eighteenth-conference-on | 2106.10886 | null | https://arxiv.org/abs/2106.10886v1 | https://arxiv.org/pdf/2106.10886v1.pdf | Proceedings Eighteenth Conference on Theoretical Aspects of Rationality and Knowledge | The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a biannual conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to further our understanding of interdisciplinary issues involving reasoning about rationality and knowledge. Topics of interest include, but are not limited to, semantic models for knowledge, belief, awareness and uncertainty, bounded rationality and resource-bounded reasoning, commonsense epistemic reasoning, epistemic logic, epistemic game theory, knowledge and action, applications of reasoning about knowledge and other mental states, belief revision, and foundations of multi-agent systems. These proceedings contain the papers that have been accepted for presentation at the Eighteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2021), held between June 25 and June 27, 2021, at Tsinghua University at Beijing, China. | ['Andrés Perea', 'Joseph Halpern'] | 2021-06-21 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-2.64327854e-01 7.01045871e-01 -1.52836412e-01 -6.15829937e-02
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abb4de10-b259-43e2-84a0-87c9ebbe3787 | greyc-fintoc-2022-handling-document-layout | null | null | https://aclanthology.org/2022.fnp-1.15 | https://aclanthology.org/2022.fnp-1.15.pdf | GREYC@FinTOC-2022: Handling Document Layout and Structure in Native PDF Bundle of Documents | n this paper, we present our contribution to the FinTOC-2022 Shared Task “Financial Document Structure Extraction”. We participated in the three tracks dedicated to English, French and Spanish document processing. Our main contribution consists in considering financial prospectus as a bundle of documents, i.e., a set of merged documents, each with their own layout and structure. Therefore, Document Layout and Structure Analysis (DLSA) first starts with the boundary detection of each document using general layout features. Then, the process applies inside each single document, taking advantage of the local properties. DLSA is achieved considering simultaneously text content, vectorial shapes and images embedded in the native PDF document. For the Title Detection task in English and French, we observed a significant improvement of the F-measures for Title Detection compared with those obtained during our previous participation. | ['Nadine Lucas', 'Emmanuel Giguet'] | null | null | null | null | fnp-lrec-2022-6 | ['boundary-detection'] | ['computer-vision'] | [ 1.39701605e-01 -5.45915589e-02 1.64380416e-01 -7.84271359e-02
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a2b8e1ea-770f-445e-84db-f4e4e6f7709a | ai-imu-dead-reckoning | 1904.06064 | null | http://arxiv.org/abs/1904.06064v1 | http://arxiv.org/pdf/1904.06064v1.pdf | AI-IMU Dead-Reckoning | In this paper we propose a novel accurate method for dead-reckoning of
wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the
context of intelligent vehicles, robust and accurate dead-reckoning based on
the IMU may prove useful to correlate feeds from imaging sensors, to safely
navigate through obstructions, or for safe emergency stops in the extreme case
of exteroceptive sensors failure. The key components of the method are the
Kalman filter and the use of deep neural networks to dynamically adapt the
noise parameters of the filter. The method is tested on the KITTI odometry
dataset, and our dead-reckoning inertial method based only on the IMU
accurately estimates 3D position, velocity, orientation of the vehicle and
self-calibrates the IMU biases. We achieve on average a 1.10% translational
error and the algorithm competes with top-ranked methods which, by contrast,
use LiDAR or stereo vision. We make our implementation open-source at:
https://github.com/mbrossar/ai-imu-dr | ['Silvère Bonnabel', 'Axel Barrau', 'Martin Brossard'] | 2019-04-12 | null | null | null | null | ['dead-reckoning-prediction'] | ['miscellaneous'] | [-4.57597733e-01 -2.14718580e-01 -1.53343707e-01 -2.94331700e-01
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b0238946-3636-452a-92e3-10cb392e51d6 | multi-label-transformer-for-action-unit | 2203.12531 | null | https://arxiv.org/abs/2203.12531v3 | https://arxiv.org/pdf/2203.12531v3.pdf | Multi-label Transformer for Action Unit Detection | Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face representations and has therefore aroused great interest in the past few years. One of the main obstacles toward building efficient deep learning based AU detection system is the lack of wide facial image databases annotated by AU experts. In that extent the ABAW challenge paves the way toward better AU detection as it involves a 2M frames AU annotated dataset. In this paper, we present our submission to the ABAW3 challenge. In a nutshell, we applied a multi-label detection transformer that leverage multi-head attention to learn which part of the face image is the most relevant to predict each AU. | ['Kevin Bailly', 'Arnaud Dapogny', 'Edouard Yvinec', 'Gauthier Tallec'] | 2022-03-23 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 3.52411121e-01 4.31614071e-01 -1.78496197e-01 -5.55869877e-01
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8dc9770b-6c73-401c-ad8e-502a3d0c9c31 | effect-of-temporal-resolution-on-the | 2302.10761 | null | https://arxiv.org/abs/2302.10761v2 | https://arxiv.org/pdf/2302.10761v2.pdf | Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing | Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. Furthermore, novel possibilities have been demonstrated, such as inferring the existence of previously unseen attractors. Sampling, in contrast, has a strong influence on such functions. Sampling is indispensable in a physical reservoir computer that uses an existing physical system as a reservoir because the use of an external digital system for the data input is usually inevitable. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series. We found, as expected, that excessively coarse sampling degrades the system performance, but also that excessively dense sampling is unsuitable. Based on quantitative indicators that capture the local and global characteristics of attractors, we identify a suitable window of the sampling frequency and discuss its underlying mechanisms. | ['Makoto Naruse', 'Ryoichi Horisaki', 'Takatomo Mihana', 'André Röhm', 'Kohei Tsuchiyama'] | 2023-01-27 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.12154552e-01 -3.84351403e-01 2.54940148e-02 2.60007262e-01
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2bc90bd7-d019-4788-bc24-d132025ab701 | spectral-inference-networks-unifying-spectral | 1806.02215 | null | https://arxiv.org/abs/1806.02215v3 | https://arxiv.org/pdf/1806.02215v3.pdf | Spectral Inference Networks: Unifying Deep and Spectral Learning | We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner. | ['Stig Petersen', 'David G. T. Barrett', 'Kimberly L. Stachenfeld', 'David Pfau', 'Ashish Agarwal'] | 2018-06-06 | spectral-inference-networks-unifying-deep-and | https://openreview.net/forum?id=SJzqpj09YQ | https://openreview.net/pdf?id=SJzqpj09YQ | iclr-2019-5 | ['variational-monte-carlo'] | ['miscellaneous'] | [ 5.22057533e-01 -1.30357184e-02 -3.56800258e-01 -2.64740586e-01
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cf0b31e1-fe9c-472b-96c3-f97149009f9e | point-convolutional-neural-networks-by | 1803.10091 | null | http://arxiv.org/abs/1803.10091v1 | http://arxiv.org/pdf/1803.10091v1.pdf | Point Convolutional Neural Networks by Extension Operators | This paper presents Point Convolutional Neural Networks (PCNN): a novel
framework for applying convolutional neural networks to point clouds. The
framework consists of two operators: extension and restriction, mapping point
cloud functions to volumetric functions and vise-versa. A point cloud
convolution is defined by pull-back of the Euclidean volumetric convolution via
an extension-restriction mechanism.
The point cloud convolution is computationally efficient, invariant to the
order of points in the point cloud, robust to different samplings and varying
densities, and translation invariant, that is the same convolution kernel is
used at all points. PCNN generalizes image CNNs and allows readily adapting
their architectures to the point cloud setting.
Evaluation of PCNN on three central point cloud learning benchmarks
convincingly outperform competing point cloud learning methods, and the vast
majority of methods working with more informative shape representations such as
surfaces and/or normals. | ['Haggai Maron', 'Yaron Lipman', 'Matan Atzmon'] | 2018-03-27 | null | null | null | null | ['classify-3d-point-clouds', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.64218205e-02 -2.53671765e-01 2.15260964e-03 -2.27344319e-01
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6bff9f6f-eca1-4541-a97d-c0f6f1dd58fe | style-transfer-counterfactual-explanations-an | null | null | https://www.sciencedirect.com/science/article/pii/S0933365722002093 | https://www.sciencedirect.com/science/article/pii/S0933365722002093/pdfft?md5=f109e9c0643e156c487a0c39375a17db&pid=1-s2.0-S0933365722002093-main.pdf | Style-transfer counterfactual explanations: An application to mortality prevention of ICU patients | In recent years, machine learning methods have been rapidly adopted in the medical domain. However, current state-of-the-art medical mining methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to developing interpretable machine learning models. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual solution MedSeqCF for preventing the mortality of three cohorts of ICU patients, by representing their electronic health records as medical event sequences, and generating counterfactuals by adopting and employing a text style-transfer technique. We propose three model augmentations for MedSeqCF to integrate additional medical knowledge for generating more trustworthy counterfactuals. Experimental results on the MIMIC-III dataset strongly suggest that augmented style-transfer methods can be effectively adapted for the problem of counterfactual explanations in healthcare applications and can further improve the model performance in terms of validity, BLEU-4, local outlier factor, and edit distance. In addition, our qualitative analysis of the results by consultation with medical experts suggests that our style-transfer solutions can generate clinically relevant and actionable counterfactual explanations. | ['Panagiotis Papapetrou', 'Vasiliki Kougia', 'Isak Samsten', 'Zhendong Wang'] | 2023-01-01 | null | null | null | artificial-intelligence-in-medicine-2023-1 | ['style-transfer', 'interpretable-machine-learning', 'counterfactual-explanation', 'text-style-transfoer'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [ 6.52115643e-01 9.62344050e-01 -2.65819311e-01 -6.20317638e-01
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4c7f4891-92a9-439d-b73c-2a5cefc16a47 | directed-acyclic-graph-network-for | 2105.12907 | null | https://arxiv.org/abs/2105.12907v2 | https://arxiv.org/pdf/2105.12907v2.pdf | Directed Acyclic Graph Network for Conversational Emotion Recognition | The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC. | ['Xiaojun Quan', 'Yunyi Yang', 'Siyue Wu', 'Weizhou Shen'] | 2021-05-27 | null | https://aclanthology.org/2021.acl-long.123 | https://aclanthology.org/2021.acl-long.123.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 7.74975345e-02 1.88127026e-01 1.44668326e-01 -7.10734367e-01
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2ceec28f-465a-4b75-9a81-30ff1f9a4032 | banmo-building-animatable-3d-neural-models | 2112.12761 | null | https://arxiv.org/abs/2112.12761v3 | https://arxiv.org/pdf/2112.12761v3.pdf | BANMo: Building Animatable 3D Neural Models from Many Casual Videos | Prior work for articulated 3D shape reconstruction often relies on specialized sensors (e.g., synchronized multi-camera systems), or pre-built 3D deformable models (e.g., SMAL or SMPL). Such methods are not able to scale to diverse sets of objects in the wild. We present BANMo, a method that requires neither a specialized sensor nor a pre-defined template shape. BANMo builds high-fidelity, articulated 3D models (including shape and animatable skinning weights) from many monocular casual videos in a differentiable rendering framework. While the use of many videos provides more coverage of camera views and object articulations, they introduce significant challenges in establishing correspondence across scenes with different backgrounds, illumination conditions, etc. Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model. We introduce neural blend skinning models that allow for differentiable and invertible articulated deformations. When combined with canonical embeddings, such models allow us to establish dense correspondences across videos that can be self-supervised with cycle consistency. On real and synthetic datasets, BANMo shows higher-fidelity 3D reconstructions than prior works for humans and animals, with the ability to render realistic images from novel viewpoints and poses. Project webpage: banmo-www.github.io . | ['Hanbyul Joo', 'Andrea Vedaldi', 'Deva Ramanan', 'Natalia Neverova', 'Minh Vo', 'Gengshan Yang'] | 2021-12-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_BANMo_Building_Animatable_3D_Neural_Models_From_Many_Casual_Videos_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_BANMo_Building_Animatable_3D_Neural_Models_From_Many_Casual_Videos_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [ 1.47795096e-01 -1.27892911e-01 9.30259451e-02 -7.13395402e-02
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192c552f-9990-4e61-8caa-09c4653620da | scaling-adversarial-training-to-large | 2210.09852 | null | https://arxiv.org/abs/2210.09852v1 | https://arxiv.org/pdf/2210.09852v1.pdf | Scaling Adversarial Training to Large Perturbation Bounds | The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending attacks constrained within low magnitude Lp norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to achieve adversarial robustness within larger bounds, against perturbations that may be perceptible, but do not change human (or Oracle) prediction. The presence of images that flip Oracle predictions and those that do not makes this a challenging setting for adversarial robustness. We discuss the ideal goals of an adversarial defense algorithm beyond perceptual limits, and further highlight the shortcomings of naively extending existing training algorithms to higher perturbation bounds. In order to overcome these shortcomings, we propose a novel defense, Oracle-Aligned Adversarial Training (OA-AT), to align the predictions of the network with that of an Oracle during adversarial training. The proposed approach achieves state-of-the-art performance at large epsilon bounds (such as an L-inf bound of 16/255 on CIFAR-10) while outperforming existing defenses (AWP, TRADES, PGD-AT) at standard bounds (8/255) as well. | ['R. Venkatesh Babu', 'Gaurang Sriramanan', 'Samyak Jain', 'Sravanti Addepalli'] | 2022-10-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.93107474e-01 3.45589548e-01 2.82563984e-01 -2.46584579e-01
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95f2b290-5cc3-44dd-b717-4149063c7d81 | a-neural-probabilistic-structured-prediction | null | null | https://aclanthology.org/P15-1117 | https://aclanthology.org/P15-1117.pdf | A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing | null | ['Shu-Jian Huang', 'Jia-Jun Chen', 'Yue Zhang', 'Hao Zhou'] | 2015-07-01 | a-neural-probabilistic-structured-prediction-1 | https://aclanthology.org/P15-1117 | https://aclanthology.org/P15-1117.pdf | ijcnlp-2015-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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be9b00fb-7750-482b-9db1-ff96f4338a04 | a-joint-learning-approach-for-semi-supervised | 2204.03208 | null | https://arxiv.org/abs/2204.03208v1 | https://arxiv.org/pdf/2204.03208v1.pdf | A Joint Learning Approach for Semi-supervised Neural Topic Modeling | Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies. | ['Finale Doshi-Velez', 'Abhishek Sharma', 'Neehal Tumma', 'Rajat Mittal', 'Jeffrey Chiu'] | 2022-04-07 | null | https://aclanthology.org/2022.spnlp-1.5 | https://aclanthology.org/2022.spnlp-1.5.pdf | spnlp-acl-2022-5 | ['topic-models'] | ['natural-language-processing'] | [-8.76257792e-02 5.03655910e-01 -5.18235803e-01 -4.97142017e-01
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f14c5865-608f-4477-bcff-129557eeaa57 | nuclei-panoptic-segmentation-and-composition | 2202.11804 | null | https://arxiv.org/abs/2202.11804v1 | https://arxiv.org/pdf/2202.11804v1.pdf | Nuclei panoptic segmentation and composition regression with multi-task deep neural networks | Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology. The Colon Nuclei Identification and Counting (CoNIC) Challenge is held to help drive forward research and innovation for automatic nuclei recognition in computational pathology. This report describes our proposed method submitted to the CoNIC challenge. Our method employs a multi-task learning framework, which performs a panoptic segmentation task and a regression task. For the panoptic segmentation task, we use encoder-decoder type deep neural networks predicting a direction map in addition to a segmentation map in order to separate neighboring nuclei into different instances | ['Satoshi Kasai', 'Satoshi Kondo'] | 2022-02-23 | null | null | null | null | ['explainable-models', 'nuclear-segmentation'] | ['computer-vision', 'medical'] | [ 5.71663857e-01 1.92103729e-01 -1.53489441e-01 -2.87544131e-01
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1fc657d7-3eba-4d51-9812-5f2d68b6ecc5 | elevater-a-benchmark-and-toolkit-for | 2204.08790 | null | https://arxiv.org/abs/2204.08790v6 | https://arxiv.org/pdf/2204.08790v6.pdf | ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models | Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a platform for Computer Vision in the Wild (CVinW), and is publicly released at at https://computer-vision-in-the-wild.github.io/ELEVATER/ | ['Yong Jae Lee', 'Zicheng Liu', 'Jianfeng Gao', 'Houdong Hu', 'Ping Jin', 'Jianwei Yang', 'Jyoti Aneja', 'Pengchuan Zhang', 'Liunian Harold Li', 'Haotian Liu', 'Chunyuan Li'] | 2022-04-19 | null | null | null | null | ['zero-shot-object-detection', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.86376078e-02 -3.46262813e-01 -3.67864341e-01 -4.10578698e-01
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4a524198-4e7c-497e-8c57-fb0fb90d6261 | online-self-attentive-gated-rnns-for-real | 2106.13493 | null | https://arxiv.org/abs/2106.13493v2 | https://arxiv.org/pdf/2106.13493v2.pdf | Online Self-Attentive Gated RNNs for Real-Time Speaker Separation | Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produce high-quality separations, they were mainly applied under offline settings, in which the model has access to the full input signal while separating the signal. In this study, we convert a non-causal state-of-the-art separation model into a causal and real-time model and evaluate its performance under both online and offline settings. We compare the performance of the proposed model to several baseline methods under anechoic, noisy, and noisy-reverberant recording conditions while exploring both monaural and binaural inputs and outputs. Our findings shed light on the relative difference between causal and non-causal models when performing separation. Our stateful implementation for online separation leads to a minor drop in performance compared to the offline model; 0.8dB for monaural inputs and 0.3dB for binaural inputs while reaching a real-time factor of 0.65. Samples can be found under the following link: https://kwanum.github.io/sagrnnc-stream-results/. | ['Anurag Kumar', 'Buye Xu', 'Zhenyu Tang', 'Yossi Adi', 'Ori Kabeli'] | 2021-06-25 | null | null | null | null | ['speaker-separation'] | ['speech'] | [-2.09678020e-02 -4.71762478e-01 3.31809103e-01 3.28662172e-02
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49b97b51-fff3-4ff2-8fa5-be7274783eff | continuously-controllable-facial-expression | 2209.08289 | null | https://arxiv.org/abs/2209.08289v1 | https://arxiv.org/pdf/2209.08289v1.pdf | Continuously Controllable Facial Expression Editing in Talking Face Videos | Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria. | ['Yong-Jin Liu', 'Yaoyuan Wang', 'Ziyang Zhang', 'Yanan sun', 'Tian Lv', 'Yu-Hui Wen', 'Zhiyao Sun'] | 2022-09-17 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.92896271e-01 -2.95528471e-02 -1.74279511e-02 -4.51963484e-01
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c3623a2b-fc8a-44a3-9e20-380ab807585b | pico-contrastive-label-disambiguation-for | 2201.08984 | null | https://arxiv.org/abs/2201.08984v3 | https://arxiv.org/pdf/2201.08984v3.pdf | PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning | Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL -- representation learning and label disambiguation -- in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning. | ['Junbo Zhao', 'Gang Chen', 'Gang Niu', 'Lei Feng', 'Yixuan Li', 'Ruixuan Xiao', 'Haobo Wang'] | 2022-01-22 | null | null | null | null | ['partial-label-learning', 'pico'] | ['methodology', 'natural-language-processing'] | [ 6.88520133e-01 2.46523917e-01 -4.30157006e-01 -4.01702613e-01
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a7bb459f-9cde-4871-b549-8d3f51325570 | digital-accessibility-and-information-mining | null | null | https://aclanthology.org/2022.wildre-1.8 | https://aclanthology.org/2022.wildre-1.8.pdf | Digital Accessibility and Information Mining of Dharmaśāstric Knowledge Traditions | The heritage of Dharmaśāstra (DS) carries extensive cultural history and encapsulates the treatises of Ancient Indian Social Institutions (SI). DS is reckoned as an epitome of the primitive Indian knowledge tradition as it incorporates a variety of genres for sciences and arts such as family law and legislation, civilization, culture, ritualistic procedures, environment, economics, commerce and finance studies, management, mathematical and medical sciences etc. SI represents a distinct tradition of civilization formation, society development and community living. The texts of the DS are primarily written in the Sanskrit language and due to its expansive subject stream, it is later translated into various other languages globally. With the ingress of the internet, the development of advanced digital technologies and IT boom, information is accessed and exchanged via digital platforms. DS texts are studied not only by Sanskrit scholars but also referred by historians, sociologists, political scientists, economists, law enthusiasts and linguists worldwide. Despite its eminence, there is a major setback in digitizing and online information mining for DS texts. The major objective of the paper is to digitize and develop an instant referencing system to amplify the digital accessibility of DS texts. This will act as an effective and immediate learning tool for researchers who are keen on intensive studying of DS concepts. | ['Subhash Chandra', 'Arooshi Nigam'] | null | null | null | null | wildre-lrec-2022-6 | ['culture'] | ['speech'] | [-1.51207000e-01 -7.85740931e-03 -3.54031414e-01 3.39044094e-01
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85a58d4f-086b-4c43-9886-042acdb810fc | low-resource-neural-headline-generation | 1707.09769 | null | http://arxiv.org/abs/1707.09769v1 | http://arxiv.org/pdf/1707.09769v1.pdf | Low-Resource Neural Headline Generation | Recent neural headline generation models have shown great results, but are
generally trained on very large datasets. We focus our efforts on improving
headline quality on smaller datasets by the means of pretraining. We propose
new methods that enable pre-training all the parameters of the model and
utilize all available text, resulting in improvements by up to 32.4% relative
in perplexity and 2.84 points in ROUGE. | ['Tanel Alumäe', 'Ottokar Tilk'] | 2017-07-31 | low-resource-neural-headline-generation-1 | https://aclanthology.org/W17-4503 | https://aclanthology.org/W17-4503.pdf | ws-2017-9 | ['headline-generation'] | ['natural-language-processing'] | [-1.10136740e-01 4.55595315e-01 -4.03457999e-01 -4.92684841e-01
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73e3c6d1-179e-48bd-949e-6562b9b03a7c | self-supervised-velocity-estimation-for | 2207.03146 | null | https://arxiv.org/abs/2207.03146v1 | https://arxiv.org/pdf/2207.03146v1.pdf | Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks | This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms of generating its own training signal for the velocities. Labels are only required for single-frame, oriented bounding boxes (OBBs). Labels for the Cartesian velocities or contiguous sequences, which are expensive to obtain, are not required. The general idea is to pre-train an object detection network without velocities using single-frame OBB labels, and then exploit the network's OBB predictions on unlabelled data for velocity training. In detail, the network's OBB predictions of the unlabelled frames are updated to the timestamp of a labelled frame using the predicted velocities and the distances between the updated OBBs of the unlabelled frame and the OBB predictions of the labelled frame are used to generate a self-supervised training signal for the velocities. The detection network architecture is extended by a module to account for the temporal relation of multiple scans and a module to represent the radars' radial velocity measurements explicitly. A two-step approach of first training only OBB detection, followed by training OBB detection and velocities is used. Further, a pre-training with pseudo-labels generated from radar radial velocity measurements bootstraps the self-supervised method of this paper. Experiments on the publicly available nuScenes dataset show that the proposed method almost reaches the velocity estimation performance of a fully supervised training, but does not require expensive velocity labels. Furthermore, we outperform a baseline method which uses only radial velocity measurements as labels. | ['Holger Blume', 'André Treptow', 'Claudius Gläser', 'Florian Faion', 'Daniel Köhler', 'Sascha Braun', 'Michael Ulrich', 'Daniel Niederlöhner'] | 2022-07-07 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 2.70999998e-01 -1.16967096e-03 -2.13893652e-01 -6.95006430e-01
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aca3e950-3a7c-449d-a89a-3f0e4aa31851 | towards-multi-instrument-drum-transcription | 1806.06676 | null | http://arxiv.org/abs/1806.06676v2 | http://arxiv.org/pdf/1806.06676v2.pdf | Towards multi-instrument drum transcription | Automatic drum transcription, a subtask of the more general automatic music
transcription, deals with extracting drum instrument note onsets from an audio
source. Recently, progress in transcription performance has been made using
non-negative matrix factorization as well as deep learning methods. However,
these works primarily focus on transcribing three drum instruments only: snare
drum, bass drum, and hi-hat. Yet, for many applications, the ability to
transcribe more drum instruments which make up standard drum kits used in
western popular music would be desirable. In this work, convolutional and
convolutional recurrent neural networks are trained to transcribe a wider range
of drum instruments. First, the shortcomings of publicly available datasets in
this context are discussed. To overcome these limitations, a larger synthetic
dataset is introduced. Then, methods to train models using the new dataset
focusing on generalization to real world data are investigated. Finally, the
trained models are evaluated on publicly available datasets and results are
discussed. The contributions of this work comprise: (i.) a large-scale
synthetic dataset for drum transcription, (ii.) first steps towards an
automatic drum transcription system that supports a larger range of instruments
by evaluating and discussing training setups and the impact of datasets in this
context, and (iii.) a publicly available set of trained models for drum
transcription. Additional materials are available at
http://ifs.tuwien.ac.at/~vogl/dafx2018 | ['Peter Knees', 'Richard Vogl', 'Gerhard Widmer'] | 2018-06-18 | null | null | null | null | ['drum-transcription', 'music-transcription'] | ['music', 'music'] | [ 7.37660304e-02 -4.11801249e-01 1.09749883e-01 2.65180022e-01
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29142f01-c538-433b-9331-ce6069d5ec06 | tell-me-what-you-read-automatic-expertise | null | null | https://aclanthology.org/2021.ranlp-main.177 | https://aclanthology.org/2021.ranlp-main.177.pdf | Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains | This paper investigates the effectiveness of automatic annotator assignment for text annotation in expert domains. In the task of creating high-quality annotated corpora, expert domains often cover multiple sub-domains (e.g. organic and inorganic chemistry in the chemistry domain) either explicitly or implicitly. Therefore, it is crucial to assign annotators to documents relevant with their fine-grained domain expertise. However, most of existing methods for crowdsoucing estimate reliability of each annotator or annotated instance only after the annotation process. To address the issue, we propose a method to estimate the domain expertise of each annotator before the annotation process using information easily available from the annotators beforehand. We propose two measures to estimate the annotator expertise: an explicit measure using the predefined categories of sub-domains, and an implicit measure using distributed representations of the documents. The experimental results on chemical name annotation tasks show that the annotation accuracy improves when both explicit and implicit measures for annotator assignment are combined. | ['Patrycja Swieczkowska', 'Rafal Rzepka', 'Kazutaka Shimada', 'Yasutaka Kumano', 'Hiroaki Yoshida', 'Kimi Kaneko', 'Tomoya Iwakura', 'Hiyori Yoshikawa'] | null | null | https://aclanthology.org/2021.ranlp-1.177 | https://aclanthology.org/2021.ranlp-1.177.pdf | ranlp-2021-9 | ['text-annotation'] | ['natural-language-processing'] | [-5.06711937e-02 6.57165825e-01 -7.76378065e-02 -5.24519503e-01
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fe6dc155-d291-4bb8-a2cc-2bc5fd647f3e | analysis-of-social-media-data-using | 2004.11838 | null | https://arxiv.org/abs/2004.11838v1 | https://arxiv.org/pdf/2004.11838v1.pdf | Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response | Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image). | ['Muhammad Imran', 'Ferda Ofli', 'Firoj Alam'] | 2020-04-14 | null | null | null | null | ['small-data', 'multimodal-text-and-image-classification'] | ['computer-vision', 'methodology'] | [ 1.63617190e-02 -1.49188787e-01 -3.89938653e-02 -2.73379147e-01
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6dc0b37a-5d6e-4cbf-bad4-c4bfc31064c5 | mira-a-computational-neuro-based-cognitive | 1902.09291 | null | http://arxiv.org/abs/1902.09291v2 | http://arxiv.org/pdf/1902.09291v2.pdf | MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems | The human mind is still an unknown process of neuroscience in many aspects.
Nevertheless, for decades the scientific community has proposed computational
models that try to simulate their parts, specific applications, or their
behavior in different situations. The most complete model in this line is
undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving
as a generic computational architecture for several applications. The present
project is inspired by the LIDA model to apply it to the process of movie
recommendation, the model called MIRA (Movie Intelligent Recommender Agent)
presented percentages of precision similar to a traditional model when
submitted to the same assay conditions. Moreover, the proposed model reinforced
the precision indexes when submitted to tests with volunteers, proving once
again its performance as a cognitive model, when executed with small data
volumes. Considering that the proposed model achieved a similar behavior to the
traditional models under conditions expected to be similar for natural systems,
it can be said that MIRA reinforces the applicability of LIDA as a path to be
followed for the study and generation of computational agents inspired by
neural behaviors. | ['Guilherme A. Wachs-Lopes', 'Amanda M. Lima', 'Paulo S. Rodrigues', 'Lucas A. Silva', 'Felipe S. Vargas', 'Mariana B. Santos'] | 2019-02-25 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-2.66603887e-01 1.86214432e-01 1.97357252e-01 -4.71830219e-02
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7acc4b83-3f7b-4382-9fa1-a1f290de9266 | zero-shot-adversarial-quantization | 2103.15263 | null | https://arxiv.org/abs/2103.15263v2 | https://arxiv.org/pdf/2103.15263v2.pdf | Zero-shot Adversarial Quantization | Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the high performance of full-precision models, most existing quantization methods focus on fine-tuning quantized model by assuming training datasets are accessible. However, this assumption sometimes is not satisfied in real situations due to data privacy and security issues, thereby making these quantization methods not applicable. To achieve zero-short model quantization without accessing training data, a tiny number of quantization methods adopt either post-training quantization or batch normalization statistics-guided data generation for fine-tuning. However, both of them inevitably suffer from low performance, since the former is a little too empirical and lacks training support for ultra-low precision quantization, while the latter could not fully restore the peculiarities of original data and is often low efficient for diverse data generation. To address the above issues, we propose a zero-shot adversarial quantization (ZAQ) framework, facilitating effective discrepancy estimation and knowledge transfer from a full-precision model to its quantized model. This is achieved by a novel two-level discrepancy modeling to drive a generator to synthesize informative and diverse data examples to optimize the quantized model in an adversarial learning fashion. We conduct extensive experiments on three fundamental vision tasks, demonstrating the superiority of ZAQ over the strong zero-shot baselines and validating the effectiveness of its main components. Code is available at <https://git.io/Jqc0y>. | ['Jun Wang', 'Wei zhang', 'Yuang Liu'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Zero-Shot_Adversarial_Quantization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.06030035e-01 -1.72937363e-02 -4.48730379e-01 -3.96261692e-01
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dfdb0ab0-03ee-4708-8b47-4ea898bc50d8 | all4one-symbiotic-neighbour-contrastive | 2303.09417 | null | https://arxiv.org/abs/2303.09417v1 | https://arxiv.org/pdf/2303.09417v1.pdf | All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction | Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities. However, their computational efficiency decreases when more than one neighbour is used. In this paper, we propose a novel contrastive SSL approach, which we call All4One, that reduces the distance between neighbour representations using ''centroids'' created through a self-attention mechanism. We use a Centroid Contrasting objective along with single Neighbour Contrasting and Feature Contrasting objectives. Centroids help in learning contextual information from multiple neighbours whereas the neighbour contrast enables learning representations directly from the neighbours and the feature contrast allows learning representations unique to the features. This combination enables All4One to outperform popular instance discrimination approaches by more than 1% on linear classification evaluation for popular benchmark datasets and obtains state-of-the-art (SoTA) results. Finally, we show that All4One is robust towards embedding dimensionalities and augmentations, surpassing NNCLR and Barlow Twins by more than 5% on low dimensionality and weak augmentation settings. The source code would be made available soon. | ['Petia Radeva', 'Bhalaji Nagarajan', 'Ignacio Sarasúa', 'Imanol G. Estepa'] | 2023-03-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.03547972e-01 1.14196494e-01 -3.06808412e-01 -2.76169568e-01
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34e36ae2-f0a8-4e69-86e4-5566842e9f05 | rolling-horizon-based-temporal-decomposition | 2303.03475 | null | https://arxiv.org/abs/2303.03475v1 | https://arxiv.org/pdf/2303.03475v1.pdf | Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows | The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. We compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances, the baseline approach is as competitive as our framework. However, in larger problem instances, our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times. | ['Samitha Samaranayake', 'Abhishek Dubey', 'Aron Laszka', 'Philip Pugliese', 'Michael Wilbur', 'Danushka Edirimanna', 'Youngseo Kim'] | 2023-03-06 | null | null | null | null | ['combinatorial-optimization', 'problem-decomposition'] | ['methodology', 'miscellaneous'] | [-1.02481544e-01 -5.42183034e-02 -4.16477323e-01 -2.40956899e-02
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bd1c705e-6cab-467b-8c70-c8110c6b69c4 | 3dvg-transformer-relation-modeling-for-visual | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf | 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds | Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world. By formulating this task as a grounding-by-detection problem, lots of recent works focus on how to exploit more powerful detectors and comprehensive language features, but (1) how to model complex relations for generating context-aware object proposals and (2) how to leverage proposal relations to distinguish the true target object from similar proposals are not fully studied yet. Inspired by the well-known transformer architecture, we propose a relation-aware visual grounding method on 3D point clouds, named as 3DVG-Transformer, to fully utilize the contextual clues for relationenhanced proposal generation and cross-modal proposal disambiguation, which are enabled by a newly designed coordinate-guided contextual aggregation (CCA) module in the object proposal generation stage, and a multiplex attention (MA) module in the cross-modal feature fusion stage. We validate that our 3DVG-Transformer outperforms the state-of-the-art methods by a large margin, on two point cloud-based visual grounding datasets, ScanRefer and Nr3D/Sr3D from ReferIt3D, especially for complex scenarios containing multiple objects of the same category. | ['Dong Xu', 'Lu Sheng', 'Daigang Cai', 'Lichen Zhao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['object-proposal-generation'] | ['computer-vision'] | [-4.48515862e-02 4.16940544e-03 1.07787669e-01 -3.78953099e-01
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05445c0d-cbd2-422e-9fbd-6ff600508287 | knowledge-base-completion-baseline-strikes | 2005.00804 | null | https://arxiv.org/abs/2005.00804v3 | https://arxiv.org/pdf/2005.00804v3.pdf | Knowledge Base Completion: Baseline strikes back (Again) | Knowledge Base Completion (KBC) has been a very active area lately. Several recent KBCpapers propose architectural changes, new training methods, or even new formulations. KBC systems are usually evaluated on standard benchmark datasets: FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Most existing methods train with a small number of negative samples for each positive instance in these datasets to save computational costs. This paper discusses how recent developments allow us to use all available negative samples for training. We show that Complex, when trained using all available negative samples, gives near state-of-the-art performance on all the datasets. We call this approach COMPLEX-V2. We also highlight how various multiplicative KBC methods, recently proposed in the literature, benefit from this train-ing regime and become indistinguishable in terms of performance on most datasets. Our work calls for a reassessment of their individual value, in light of these findings. | ['Sushant Rathi', 'Prachi Jain', 'Mausam', 'Soumen Chakrabarti'] | 2020-05-02 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'knowledge-base-population'] | ['graphs', 'knowledge-base', 'natural-language-processing'] | [-8.20567980e-02 -1.73425172e-02 -5.38488150e-01 -3.53076935e-01
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8c8d8d39-2ecb-45dd-8572-0348aee27c83 | commonality-in-recommender-systems-evaluating | 2302.11360 | null | https://arxiv.org/abs/2302.11360v2 | https://arxiv.org/pdf/2302.11360v2.pdf | Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship | Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimize for personalized user experience, they can overlook impacts on cultural experience in the aggregate. After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content. We developed commonality through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of content toward a shared cultural experience across a population of users. We empirically compare the performance of recommendation algorithms using commonality with existing metrics, demonstrating that commonality captures a novel property of system behavior complementary to existing metrics. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing `public good' rationales for machine learning systems. | ['Georgina Born', 'Fernando Diaz', 'Gustavo Ferreira', 'Andres Ferraro'] | 2023-02-22 | null | null | null | null | ['culture'] | ['speech'] | [-2.48317644e-01 -8.50730389e-02 -4.62186784e-01 -1.12890840e-01
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6f785ccd-d56b-4449-9dac-a9f5f37efbc3 | actions-generation-from-captions | 1902.11109 | null | http://arxiv.org/abs/1902.11109v1 | http://arxiv.org/pdf/1902.11109v1.pdf | Actions Generation from Captions | Sequence transduction models have been widely explored in many natural
language processing tasks. However, the target sequence usually consists of
discrete tokens which represent word indices in a given vocabulary. We barely
see the case where target sequence is composed of continuous vectors, where
each vector is an element of a time series taken successively in a temporal
domain. In this work, we introduce a new data set, named Action Generation Data
Set (AGDS) which is specifically designed to carry out the task of
caption-to-action generation. This data set contains caption-action pairs. The
caption is comprised of a sequence of words describing the interactive movement
between two people, and the action is a captured sequence of poses representing
the movement. This data set is introduced to study the ability of generating
continuous sequences through sequence transduction models. We also propose a
model to innovatively combine Multi-Head Attention (MHA) and Generative
Adversarial Network (GAN) together. In our model, we have one generator to
generate actions from captions and three discriminators where each of them is
designed to carry out a unique functionality: caption-action consistency
discriminator, pose discriminator and pose transition discriminator. This novel
design allowed us to achieve plausible generation performance which is
demonstrated in the experiments. | ['Yida Xu', 'Xuan Liang'] | 2019-02-14 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 8.04416656e-01 4.39627916e-02 3.04750681e-01 -2.37997979e-01
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36e0fe95-bd9a-4ab8-9dcd-2c9ec6d1e7a9 | opinion-aspect-extraction-in-dutch-childrens | 1910.10502 | null | https://arxiv.org/abs/1910.10502v1 | https://arxiv.org/pdf/1910.10502v1.pdf | Opinion aspect extraction in Dutch childrens diary entries | Aspect extraction can be used in dialogue systems to understand the topic of opinionated text. Expressing an empathetic reaction to an opinion can strengthen the bond between a human and, for example, a robot. The aim of this study is three-fold: 1. create a new annotated dataset for both aspect extraction and opinion words for Dutch childrens language, 2. acquire aspect extraction results for this task and 3. improve current results for aspect extraction in Dutch reviews. This was done by training a deep learning Gated Recurrent Unit (GRU) model, originally developed for an English review dataset, on Dutch restaurant review data to classify both opinion words and their respective aspects. We obtained state-of-the-art performance on the Dutch restaurant review dataset. Additionally, we acquired aspect extraction results for the Dutch childrens dataset. Since the model was trained on standardised language, these results are quite promising. | ['Maaike H. T. de Boer', 'Hella Haanstra'] | 2019-10-21 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [-9.03246850e-02 9.51419532e-01 -2.74123866e-02 -8.28817666e-01
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16aa8e9d-2d17-49f7-b2e6-5606571adece | learning-to-rank-with-partitioned-preference | 2006.05067 | null | https://arxiv.org/abs/2006.05067v3 | https://arxiv.org/pdf/2006.05067v3.pdf | Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model | We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given $N$ items with $M$ partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of $O(N+S!)$, where $S$ is the maximum size of the top $M-1$ partitions. This computational challenge restrains most existing PL-based listwise LTR methods to a special case of partitioned preference, top-$K$ ranking, where the exact order of the top $K$ items is known. In this paper, we exploit a random utility model formulation of the PL model, and propose an efficient numerical integration approach for calculating the likelihood and its gradients with a time complexity $O(N+S^3)$. We demonstrate that the proposed method outperforms well-known LTR baselines and remains scalable through both simulation experiments and applications to real-world eXtreme Multi-Label classification tasks. | ['Xinyang Yi', 'Lichan Hong', 'Weijing Tang', 'Zhe Zhao', 'Jiaqi Ma', 'Qiaozhu Mei', 'Ed H. Chi'] | 2020-06-09 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.68016230e-03 -4.26985294e-01 -4.95563269e-01 -6.09784782e-01
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2eae98a3-ad69-4970-be91-e419a2453c43 | bcnet-a-deep-convolutional-neural-network-for | 2107.05037 | null | https://arxiv.org/abs/2107.05037v1 | https://arxiv.org/pdf/2107.05037v1.pdf | BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading | Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman. In order to provide effective treatment or prevention of this cancer, disease diagnosis in the early stages would be of high importance. There have been various methods to detect this disorder in which using images have to play a dominant role. Deep learning has been recently adopted widely in different areas of science, especially medicine. In breast cancer detection problems, some diverse deep learning techniques have been developed on different datasets and resulted in good accuracy. In this article, we aimed to present a deep neural network model to classify histopathological images from the Databiox image dataset as the first application on this image database. Our proposed model named BCNet has taken advantage of the transfer learning approach in which VGG16 is selected from available pertained models as a feature extractor. Furthermore, to address the problem of insufficient data, we employed the data augmentation technique to expand the input dataset. All implementations in this research, ranging from pre-processing actions to depicting the diagram of the model architecture, have been carried out using tf.keras API. As a consequence of the proposed model execution, the significant validation accuracy of 88% and evaluation accuracy of 72% obtained. | ['Hamidreza Bolhasani', 'Atefeh Safayari', 'Pouya Hallaj Zavareh'] | 2021-07-11 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.36311883e-01 8.27799141e-02 1.09865487e-01 -2.83993036e-01
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51868dda-cc6a-4ba9-9398-b32ddeff5991 | learn-to-adapt-for-generalized-zero-shot-text-1 | null | null | https://aclanthology.org/2022.acl-long.39 | https://aclanthology.org/2022.acl-long.39.pdf | Learn to Adapt for Generalized Zero-Shot Text Classification | Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationary in predicting procedures. To address these challenges, we propose a novel Learn to Adapt (LTA) network using a variant meta-learning framework. Specifically, LTA trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning (GZSL) scenario in accordance with the test time, and simultaneously learns to calibrate the class prototypes and sample representations to make the learned parameters adaptive to incoming unseen classes. We claim that the proposed model is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space. Extensive experiments on five text classification datasets show that our model outperforms several competitive previous approaches by large margins. The code and the whole datasets are available at https://github.com/Quareia/LTA. | ['Yongbin Liu', 'Ziwei Bai', 'Xiaojie Wang', 'Caixia Yuan', 'Yiwen Zhang'] | null | null | null | null | acl-2022-5 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.65357882e-01 -9.56637412e-02 -4.01287079e-01 -6.09833181e-01
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d06a4da3-13f8-41ea-aa90-391fe1b0da2e | spatio-temporal-self-supervised | 2109.00179 | null | https://arxiv.org/abs/2109.00179v1 | https://arxiv.org/pdf/2109.00179v1.pdf | Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds | To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc. In this paper, we tackle this challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in the wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL takes two temporally-correlated frames from a 3D point cloud sequence as the input, transforms it with the spatial data augmentation, and learns the invariant representation self-supervisedly. To corroborate the efficacy of STRL, we conduct extensive experiments on three types (synthetic, indoor, and outdoor) of datasets. Experimental results demonstrate that, compared with supervised learning methods, the learned self-supervised representation facilitates various models to attain comparable or even better performances while capable of generalizing pre-trained models to downstream tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. Moreover, the spatio-temporal contextual cues embedded in 3D point clouds significantly improve the learned representations. | ['Yixin Zhu', 'Song-Chun Zhu', 'Yichen Xie', 'Siyuan Huang'] | 2021-09-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_Spatio-Temporal_Self-Supervised_Representation_Learning_for_3D_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_Spatio-Temporal_Self-Supervised_Representation_Learning_for_3D_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-retrieval', '3d-point-cloud-linear-classification', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.68255681e-01 -1.21674508e-01 -2.17987731e-01 -6.70747280e-01
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4314b2de-840a-4820-b7b7-ee8d676231bd | towards-human-evaluation-of-mutual | null | null | https://aclanthology.org/2022.humeval-1.10 | https://aclanthology.org/2022.humeval-1.10.pdf | Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English | Current evaluation practices for social dialog systems, dedicated to human-computer spontaneous conversation, exclusively focus on the quality of system-generated surface text, but not human-verifiable aspects of mutual understanding between the systems and their interlocutors. This work proposes Word Sense Disambiguation (WSD) as an essential component of a valid and reliable human evaluation framework, whose long-term goal is to radically improve the usability of dialog systems in real-life human-computer collaboration. The practicality of this proposal is proved via experimentally investigating (1) the WordNet 3.0 sense inventory coverage of lexical meanings in spontaneous conversation between humans in American English, assumed as an upper bound of lexical diversity of human-computer communication, and (2) the effectiveness of state-of-the-art WSD models and pretrained transformer-based contextual embeddings on this type of data. | ['Alex Lưu'] | null | null | null | null | humeval-acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.61301047e-01 4.41864818e-01 2.04183370e-01 -1.93073094e-01
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d371ae50-0807-4830-84a1-e10c0a72a4fe | event-event-relation-extraction-using-1 | null | null | https://aclanthology.org/2022.acl-short.26 | https://aclanthology.org/2022.acl-short.26.pdf | Event-Event Relation Extraction using Probabilistic Box Embedding | To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line “died” after “storm”, then it is obvious that the “storm” happened before the “died”. Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection. | ['Andrew McCallum', 'Dongxu Zhang', 'Dhruvesh Patel', 'Tianyi Yang', 'Jay-Yoon Lee', 'EunJeong Hwang'] | null | null | null | null | acl-2022-5 | ['event-relation-extraction'] | ['natural-language-processing'] | [ 3.11525822e-01 4.68133241e-01 -4.91511911e-01 -4.29422349e-01
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