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129b931c-4669-41ec-9959-e73a538a1dd1 | vuldeepecker-a-deep-learning-based-system-for-1 | 2001.02334 | null | https://arxiv.org/abs/2001.02334v1 | https://arxiv.org/pdf/2001.02334v1.pdf | $μ$VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection | Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed $\mu$VulDeePecker. The key insight underlying $\mu$VulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of $\mu$VulDeePecker. Experimental results show that $\mu$VulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. | ['Hai Jin', 'Deqing Zou', 'Sujuan Wang', 'Zhen Li', 'Shouhuai Xu'] | 2020-01-08 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.25786701e-01 -3.44362050e-01 -4.07746166e-01 -1.47433996e-01
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e22059c5-e021-4324-a35d-194a1c1c41a5 | textformer-a-query-based-end-to-end-text | 2306.03377 | null | https://arxiv.org/abs/2306.03377v1 | https://arxiv.org/pdf/2306.03377v1.pdf | TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision | End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an Adaptive Global aGgregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the sub-optimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%. | ['Jianbing Shen', 'Xingping Dong', 'Sanyuan Zhao', 'Xiameng Qin', 'Xiaoqiang Zhang', 'Yukun Zhai'] | 2023-06-06 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 4.61667329e-01 -1.14745699e-01 -1.52274370e-01 -6.67074025e-01
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fec2b315-86da-468c-95e7-b7e7428873ad | nirdizati-an-advanced-predictive-process | 2210.09688 | null | https://arxiv.org/abs/2210.09688v1 | https://arxiv.org/pdf/2210.09688v1.pdf | Nirdizati: an Advanced Predictive Process Monitoring Toolkit | Predictive Process Monitoring is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and users in analyzing, comparing and selecting the techniques that are the most suitable for them. Nirdizati is a dedicated tool for supporting users in building, comparing, analyzing, and explaining predictive models that can then be used to perform predictions on the future of an ongoing case. By providing a rich set of different state-of-the-art approaches, Nirdizati offers BPM researchers and practitioners a useful and flexible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present the current version of Nirdizati, together with its architecture which has been developed to improve its modularity and scalability. The features of Nirdizati enrich its capability to support researchers and practitioners within the entire pipeline for constructing reliable Predictive Process Monitoring models. | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi'] | 2022-10-18 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.83914232e-01 8.70049670e-02 -8.71599987e-02 -1.92829132e-01
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a9ce3266-57d1-4ee2-b6ac-b5f1200d298b | image-segmentation-and-processing-for | 1803.04620 | null | http://arxiv.org/abs/1803.04620v1 | http://arxiv.org/pdf/1803.04620v1.pdf | Image Segmentation and Processing for Efficient Parking Space Analysis | In this paper, we develop a method to detect vacant parking spaces in an
environment with unclear segments and contours with the help of MATLAB image
processing capabilities. Due to the anomalies present in the parking spaces,
such as uneven illumination, distorted slot lines and overlapping of cars. The
present-day conventional algorithms have difficulties processing the image for
accurate results. The algorithm proposed uses a combination of image
pre-processing and false contour detection techniques to improve the detection
efficiency. The proposed method also eliminates the need to employ individual
sensors to detect a car, instead uses real-time static images to consider a
group of slots together, instead of the usual single slot method. This greatly
decreases the expenses required to design an efficient parking system. We
compare the performance of our algorithm to that of other techniques. These
comparisons show that the proposed algorithm can detect the vacancies in the
parking spots while ignoring the false data and other distortions. | ['N Ruban Rajesh Kumar Muthu', 'Chetan Sai Tutika', 'Karthik R', 'Bharath KP', 'Charan Vallapaneni'] | 2018-03-13 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.29969874e-01 -2.11246639e-01 3.90190423e-01 -2.53775418e-01
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9ef501ab-1072-417b-b364-7bf08a9ebb4c | deepswir-a-deep-learning-based-approach-for | 1905.02749 | null | https://arxiv.org/abs/1905.02749v1 | https://arxiv.org/pdf/1905.02749v1.pdf | DeepSWIR: A Deep Learning Based Approach for the Synthesis of Short-Wave InfraRed Band using Multi-Sensor Concurrent Datasets | Convolutional Neural Network (CNN) is achieving remarkable progress in various computer vision tasks. In the past few years, the remote sensing community has observed Deep Neural Network (DNN) finally taking off in several challenging fields. In this study, we propose a DNN to generate a predefined High Resolution (HR) synthetic spectral band using an ensemble of concurrent Low Resolution (LR) bands and existing HR bands. Of particular interest, the proposed network, namely DeepSWIR, synthesizes Short-Wave InfraRed (SWIR) band at 5m Ground Sampling Distance (GSD) using Green (G), Red (R) and Near InfraRed (NIR) bands at both 24m and 5m GSD, and SWIR band at 24m GSD. To our knowledge, the highest spatial resolution of commercially deliverable SWIR band is at 7.5m GSD. Also, we propose a Gaussian feathering based image stitching approach in light of processing large satellite imagery. To experimentally validate the synthesized HR SWIR band, we critically analyse the qualitative and quantitative results produced by DeepSWIR using state-of-the-art evaluation metrics. Further, we convert the synthesized DN values to Top Of Atmosphere (TOA) reflectance and compare with the corresponding band of Sentinel-2B. Finally, we show one real world application of the synthesized band by using it to map wetland resources over our region of interest. | ['S Manthira Moorthi', 'Indranil Mishra', 'Yatharath Bhateja', 'Debjyoti Dhar', 'Ankur Garg', 'Litu Rout'] | 2019-05-07 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.79063678e-01 -2.24395275e-01 2.94421315e-01 -3.45812172e-01
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dd8589c3-2ae1-426d-a0ea-557f0f94edcf | reciprocal-sequential-recommendation | 2306.14712 | null | https://arxiv.org/abs/2306.14712v1 | https://arxiv.org/pdf/2306.14712v1.pdf | Reciprocal Sequential Recommendation | Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties. Although dynamic user modeling has been well-studied in sequential recommender systems, existing solutions are developed in a user-oriented manner. Therefore, it is non-trivial to adapt sequential recommendation algorithms to reciprocal recommendation. In this paper, we formulate RRS as a distinctive sequence matching task, and further propose a new approach ReSeq for RRS, which is short for Reciprocal Sequential recommendation. To capture dual-perspective matching, we propose to learn fine-grained sequence similarities by co-attention mechanism across different time steps. Further, to improve the inference efficiency, we introduce the self-distillation technique to distill knowledge from the fine-grained matching module into the more efficient student module. In the deployment stage, only the efficient student module is used, greatly speeding up the similarity computation. Extensive experiments on five real-world datasets from two scenarios demonstrate the effectiveness and efficiency of the proposed method. Our code is available at https://github.com/RUCAIBox/ReSeq/. | ['HengShu Zhu', 'Yang song', 'Wayne Xin Zhao', 'Yupeng Hou', 'Bowen Zheng'] | 2023-06-26 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 4.40927520e-02 -3.74152780e-01 -3.63448650e-01 -5.89723110e-01
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ef5681a8-dfcc-43e6-ade1-41490f8314bc | learning-to-revise-references-for-faithful | 2204.10290 | null | https://arxiv.org/abs/2204.10290v2 | https://arxiv.org/pdf/2204.10290v2.pdf | Learning to Revise References for Faithful Summarization | In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news corpora, removing low quality samples has been shown to reduce model hallucinations. Yet, for smaller, and/or noisier corpora, filtering is detrimental to performance. To improve reference quality while retaining all data, we propose a new approach: to selectively re-write unsupported reference sentences to better reflect source data. We automatically generate a synthetic dataset of positive and negative revisions by corrupting supported sentences and learn to revise reference sentences with contrastive learning. The intensity of revisions is treated as a controllable attribute so that, at inference, diverse candidates can be over-generated-then-rescored to balance faithfulness and abstraction. To test our methods, we extract noisy references from publicly available MIMIC-III discharge summaries for the task of hospital-course summarization, and vary the data on which models are trained. According to metrics and human evaluation, models trained on revised clinical references are much more faithful, informative, and fluent than models trained on original or filtered data. | ['Noémie Elhadad', 'Kathleen McKeown', 'Christopher Winestock', 'Qing Sun', 'Han-Chin Shing', 'Griffin Adams'] | 2022-04-13 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 6.37853444e-01 6.42081559e-01 -3.57810169e-01 -3.55515331e-01
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8710f70e-2243-4420-ae13-f5d6fd850b5c | redwine-a-clinical-datamart-with-text | 2304.05929 | null | https://arxiv.org/abs/2304.05929v1 | https://arxiv.org/pdf/2304.05929v1.pdf | ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research | Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face several challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes. | ['Yanshan Wang', 'Elizabeth Skidmore', 'Anthony Delitto', 'Michael J. Becich', 'Jonathan C. Silverstein', 'Brian McLay', 'Shyam Visweswaran Nickie Cappella', 'Janet Freburger', 'Allyn Bove', 'Andi Saptono', 'Bambang Parmanto', 'David Oniani'] | 2023-04-12 | null | null | null | null | ['data-visualization', 'data-visualization'] | ['methodology', 'miscellaneous'] | [-6.01565838e-02 -3.43495756e-01 -9.32156205e-01 -1.14069134e-01
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c6f4e1c5-f5c0-48e1-acb1-60b082d4416c | memd-absa-a-multi-element-multi-domain | 2306.16956 | null | https://arxiv.org/abs/2306.16956v1 | https://arxiv.org/pdf/2306.16956v1.pdf | MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with explicit and implicit aspects and opinions for ABSA research. Meanwhile, we evaluate generative and non-generative baselines on multiple ABSA subtasks under the open domain setting, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed. The datasets are publicly released at \url{https://github.com/NUSTM/MEMD-ABSA}. | ['Rui Xia', 'Jianfei Yu', 'Shijie Liu', 'Siwei Wu', 'Ke Li', 'Qiankun Zhao', 'Qiming Xie', 'Zengzhi Wang', 'Nan Song', 'Hongjie Cai'] | 2023-06-29 | null | null | null | null | ['sentiment-analysis', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [-1.14124920e-02 4.36693318e-02 -1.54699281e-01 -8.41833830e-01
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82116bfb-5519-4eee-9c41-f3150cf66080 | dula-net-a-dual-projection-network-for | 1811.11977 | null | http://arxiv.org/abs/1811.11977v2 | http://arxiv.org/pdf/1811.11977v2.pdf | DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama | We present a deep learning framework, called DuLa-Net, to predict
Manhattan-world 3D room layouts from a single RGB panorama. To achieve better
prediction accuracy, our method leverages two projections of the panorama at
once, namely the equirectangular panorama-view and the perspective
ceiling-view, that each contains different clues about the room layouts. Our
network architecture consists of two encoder-decoder branches for analyzing
each of the two views. In addition, a novel feature fusion structure is
proposed to connect the two branches, which are then jointly trained to predict
the 2D floor plans and layout heights. To learn more complex room layouts, we
introduce the Realtor360 dataset that contains panoramas of Manhattan-world
room layouts with different numbers of corners. Experimental results show that
our work outperforms recent state-of-the-art in prediction accuracy and
performance, especially in the rooms with non-cuboid layouts. | ['Hung-Kuo Chu', 'Min Sun', 'Peter Wonka', 'Chi-Han Peng', 'Fu-En Wang', 'Shang-Ta Yang'] | 2018-11-29 | dula-net-a-dual-projection-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DuLa-Net_A_Dual-Projection_Network_for_Estimating_Room_Layouts_From_a_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_DuLa-Net_A_Dual-Projection_Network_for_Estimating_Room_Layouts_From_a_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [-8.97889212e-02 9.38274190e-02 4.23099607e-01 -7.65431345e-01
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08bb7a56-4973-4dd7-8fec-bc76ef79c2ee | zero-shot-learning-of-a-conditional | 2210.14392 | null | https://arxiv.org/abs/2210.14392v1 | https://arxiv.org/pdf/2210.14392v1.pdf | Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization | We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In particular, the conditional generator is trained to produce labeled synthetic samples whose characteristics mimic the original training data by using the statistics stored in the batch normalization layers of the pre-trained model. We show the usefulness of ZS-CGAN in data-free quantization of deep neural networks. We achieved the state-of-the-art data-free network quantization of the ResNet and MobileNet classification models trained on the ImageNet dataset. Data-free quantization using ZS-CGAN showed a minimal loss in accuracy compared to that obtained by conventional data-dependent quantization. | ['Jungwon Lee', 'Mostafa El-Khamy', 'Yoojin Choi'] | 2022-10-26 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 4.57852453e-01 4.51785594e-01 8.48696604e-02 -5.65467000e-01
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5e08ab41-bd62-455f-b36d-e4f2e1002247 | metafill-text-infilling-for-meta-path | 2210.07488 | null | https://arxiv.org/abs/2210.07488v1 | https://arxiv.org/pdf/2210.07488v1.pdf | MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks | Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types, is the core technique to embed HINs. Since manually curating meta-paths is time-consuming, there is a pressing need to develop automated meta-path generation approaches. Existing meta-path generation approaches cannot fully exploit the rich textual information in HINs, such as node names and edge type names. To address this problem, we propose MetaFill, a text-infilling-based approach for meta-path generation. The key idea of MetaFill is to formulate meta-path identification problem as a word sequence infilling problem, which can be advanced by Pretrained Language Models (PLMs). We observed the superior performance of MetaFill against existing meta-path generation methods and graph embedding methods that do not leverage meta-paths in both link prediction and node classification on two real-world HIN datasets. We further demonstrated how MetaFill can accurately classify edges in the zero-shot setting, where existing approaches cannot generate any meta-paths. MetaFill exploits PLMs to generate meta-paths for graph embedding, opening up new avenues for language model applications in graph analysis. | ['Sheng Wang', 'Ming Zhang', 'Hanwen Xu', 'Junwei Yang', 'Kefei Duan', 'Zequn Liu'] | 2022-10-14 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 3.06895524e-01 3.41517001e-01 -6.68264627e-01 9.98095497e-02
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2ece5a6b-3133-4976-949b-bf5805ab4f5a | an-ensemble-of-density-based-geometric-one | 2011.06388 | null | https://arxiv.org/abs/2011.06388v2 | https://arxiv.org/pdf/2011.06388v2.pdf | An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm | One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy and interpretability for user are considered as trade-off in OCC methods. Classifier based on Hyper-Rectangle (H-RTGL) is a sort of classifier that can be a remedy for such trade-off and uses H-RTGL formulated by conjunction of geometric rules called interval. This interval can be basis of interpretability since it can be easily understood by user. However, existing H-RTGL based OCC classifiers have limitations that (i) most of them cannot reflect density of target class and (ii) that considering density has primitive interval generation method, and (iii) there exists no systematic procedure for hyperparameter of H-RTGL based OCC classifier, which influences classification performance of classifier. Based on these remarks, we suggest One-Class Hyper-Rectangle Descriptor based on density (1-HRD_d) with more elaborate interval generation method including parametric and nonparametric approaches. In addition, we designed Genetic Algorithm (GA) that consists of chromosome structure and genetic operators for systematic generation of 1-HRD_d by optimization of hyperparameter. Our work is validated through a numerical experiment using actual data set with comparison of existing OCC algorithms along with other H-RTGL based classifiers. | ['Jin Young Choi', 'Do Gyun Kim'] | 2020-10-02 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-9.41843316e-02 -1.57983243e-01 -1.72427565e-01 -4.13543880e-01
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4c263cae-7fa6-4bf9-8354-8d0c0b8b4c0b | hierarchical-scene-parsing-by-weakly | 1709.09490 | null | http://arxiv.org/abs/1709.09490v2 | http://arxiv.org/pdf/1709.09490v2.pdf | Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions | This paper investigates a fundamental problem of scene understanding: how to
parse a scene image into a structured configuration (i.e., a semantic object
hierarchy with object interaction relations). We propose a deep architecture
consisting of two networks: i) a convolutional neural network (CNN) extracting
the image representation for pixel-wise object labeling and ii) a recursive
neural network (RsNN) discovering the hierarchical object structure and the
inter-object relations. Rather than relying on elaborative annotations (e.g.,
manually labeled semantic maps and relations), we train our deep model in a
weakly-supervised learning manner by leveraging the descriptive sentences of
the training images. Specifically, we decompose each sentence into a semantic
tree consisting of nouns and verb phrases, and apply these tree structures to
discover the configurations of the training images. Once these scene
configurations are determined, then the parameters of both the CNN and RsNN are
updated accordingly by back propagation. The entire model training is
accomplished through an Expectation-Maximization method. Extensive experiments
show that our model is capable of producing meaningful scene configurations and
achieving more favorable scene labeling results on two benchmarks (i.e., PASCAL
VOC 2012 and SYSU-Scenes) compared with other state-of-the-art
weakly-supervised deep learning methods. In particular, SYSU-Scenes contains
more than 5000 scene images with their semantic sentence descriptions, which is
created by us for advancing research on scene parsing. | ['WangMeng Zuo', 'Guangrun Wang', 'Liang Lin', 'Meng Wang', 'Ruimao Zhang'] | 2017-09-27 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 5.80233037e-01 2.16603160e-01 -4.14546989e-02 -9.01173711e-01
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0f72071d-19a2-4602-a7ed-9227b0fd2135 | amc-net-an-effective-network-for-automatic | 2304.00445 | null | https://arxiv.org/abs/2304.00445v1 | https://arxiv.org/pdf/2304.00445v1.pdf | AMC-Net: An Effective Network for Automatic Modulation Classification | Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming traditional feature engineering techniques. However, AMC still has limitations in low signal-to-noise ratio (SNR) environments. To address the drawback, we propose a novel AMC-Net that improves recognition by denoising the input signal in the frequency domain while performing multi-scale and effective feature extraction. Experiments on two representative datasets demonstrate that our model performs better in efficiency and effectiveness than the most current methods. | ['Shuyuan Yang', 'Zhixi Feng', 'Tiantian Wang', 'Jiawei Zhang'] | 2023-04-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.63023567e-01 -6.89757168e-01 -3.19358140e-01 -1.44180045e-01
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6b5b5c86-284a-493a-a7e6-dab4a2e0900f | abstractive-text-summarization-for-sanskrit | null | null | https://aclanthology.org/2020.wildre-1.11 | https://aclanthology.org/2020.wildre-1.11.pdf | Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches | The authors present a work-in-progress in the field of Abstractive Text Summarization (ATS) for Sanskrit Prose {--} a first attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the language. There are three goals of SATS - to make manuscript summaries, to enrich the semantic processing of Sanskrit, and to improve the information retrieval systems in the language. While Extractive Text Summarization (ETS) is an important method, the summaries it generates are not always coherent. For qualitative coherent summaries, ATS is considered a better option by scholars. This paper reviews various ATS/ETS approaches for Sanskrit and other Indian Languages done till date. In the preliminary overview, authors conclude that of the two available approaches - structure-based and semantic-based - the latter would be viable owing to the rich morphology of Sanskrit. Moreover, a graph-based method may also be suitable. The second suggested method is the supervised-learning method. The authors also suggest attempting cross-lingual summarization as an extension to this work in future. | ['Girish Jha', 'Shagun Sinha'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.79282898e-01 2.44193733e-01 -2.16653496e-01 -1.34915382e-01
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81ce36ea-8799-4659-8c12-3c5423a8f678 | winning-arguments-interaction-dynamics-and | 1602.01103 | null | http://arxiv.org/abs/1602.01103v2 | http://arxiv.org/pdf/1602.01103v2.pdf | Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions | Changing someone's opinion is arguably one of the most important challenges
of social interaction. The underlying process proves difficult to study: it is
hard to know how someone's opinions are formed and whether and how someone's
views shift. Fortunately, ChangeMyView, an active community on Reddit, provides
a platform where users present their own opinions and reasoning, invite others
to contest them, and acknowledge when the ensuing discussions change their
original views. In this work, we study these interactions to understand the
mechanisms behind persuasion.
We find that persuasive arguments are characterized by interesting patterns
of interaction dynamics, such as participant entry-order and degree of
back-and-forth exchange. Furthermore, by comparing similar counterarguments to
the same opinion, we show that language factors play an essential role. In
particular, the interplay between the language of the opinion holder and that
of the counterargument provides highly predictive cues of persuasiveness.
Finally, since even in this favorable setting people may not be persuaded, we
investigate the problem of determining whether someone's opinion is susceptible
to being changed at all. For this more difficult task, we show that stylistic
choices in how the opinion is expressed carry predictive power. | ['Cristian Danescu-Niculescu-Mizil', 'Vlad Niculae', 'Lillian Lee', 'Chenhao Tan'] | 2016-02-02 | null | null | null | null | ['persuasion-strategies'] | ['computer-vision'] | [ 1.26772285e-01 3.62406582e-01 -3.55910242e-01 -3.62681359e-01
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868c13bb-0db5-46b3-8f1d-283456d8c50e | boltvos-box-level-tracking-for-video-object | 1904.04552 | null | https://arxiv.org/abs/1904.04552v2 | https://arxiv.org/pdf/1904.04552v2.pdf | BoLTVOS: Box-Level Tracking for Video Object Segmentation | We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation. Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect the object of interest, a temporal consistency rescoring algorithm, and a Box2Seg network that converts bounding boxes to segmentation masks. BoLTVOS performs VOS using only the firstframe bounding box without the mask. We evaluate our approach on DAVIS 2017 and YouTube-VOS, and show that it outperforms all methods that do not perform first-frame fine-tuning. We further present BoLTVOS-ft, which learns to segment the object in question using the first-frame mask while it is being tracked, without increasing the runtime. BoLTVOS-ft outperforms PReMVOS, the previously best performing VOS method on DAVIS 2016 and YouTube-VOS, while running up to 45 times faster. Our bounding box tracker also outperforms all previous short-term and longterm trackers on the bounding box level tracking datasets OTB 2015 and LTB35. A newer version of this work can be found at arXiv:1911.12836. | ['Jonathon Luiten', 'Bastian Leibe', 'Paul Voigtlaender'] | 2019-04-09 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.51072398e-01 1.41952392e-02 -3.80353719e-01 -1.39066547e-01
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4838caa0-1e36-4e61-a277-a771fd11967c | abb-bert-a-bert-model-for-disambiguating | 2207.04008 | null | https://arxiv.org/abs/2207.04008v1 | https://arxiv.org/pdf/2207.04008v1.pdf | ABB-BERT: A BERT model for disambiguating abbreviations and contractions | Abbreviations and contractions are commonly found in text across different domains. For example, doctors' notes contain many contractions that can be personalized based on their choices. Existing spelling correction models are not suitable to handle expansions because of many reductions of characters in words. In this work, we propose ABB-BERT, a BERT-based model, which deals with an ambiguous language containing abbreviations and contractions. ABB-BERT can rank them from thousands of options and is designed for scale. It is trained on Wikipedia text, and the algorithm allows it to be fine-tuned with little compute to get better performance for a domain or person. We are publicly releasing the training dataset for abbreviations and contractions derived from Wikipedia. | ['Nimit Jain', 'Aswin Gridhar Subramanian', 'Andi Cupallari', 'Prateek Kacker'] | 2022-07-08 | null | https://aclanthology.org/2021.icon-main.35 | https://aclanthology.org/2021.icon-main.35.pdf | icon-2021-12 | ['spelling-correction'] | ['natural-language-processing'] | [-5.82020544e-02 -3.49632017e-02 -2.64828414e-01 -4.28347528e-01
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5650a1f1-b933-45f0-93e6-75969e7337b1 | neural-network-applications-in-earthquake | 1910.01178 | null | https://arxiv.org/abs/1910.01178v1 | https://arxiv.org/pdf/1910.01178v1.pdf | Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations | In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We review the entire literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994-2019 period) and find two emerging trends: an increasing interest in this domain, and a complexification of ANN models over time, towards deep learning. Despite apparent positive results observed in this corpus, we demonstrate that simpler models seem to offer similar predictive powers, if not better ones. Due to the structured, tabulated nature of earthquake catalogues, and the limited number of features so far considered, simpler and more transparent machine learning models seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of Statistical Seismology, which have minimal abilities to predict large earthquakes. | ['Arnaud Mignan', 'Marco Broccardo'] | 2019-10-02 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [-4.15572196e-01 1.70888454e-01 1.05280891e-01 -1.22274272e-01
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2abdd492-c965-482d-bb91-48145a81e374 | group-activity-recognition-in-basketball | 2209.00451 | null | https://arxiv.org/abs/2209.00451v1 | https://arxiv.org/pdf/2209.00451v1.pdf | Group Activity Recognition in Basketball Tracking Data -- Neural Embeddings in Team Sports (NETS) | Like many team sports, basketball involves two groups of players who engage in collaborative and adversarial activities to win a game. Players and teams are executing various complex strategies to gain an advantage over their opponents. Defining, identifying, and analyzing different types of activities is an important task in sports analytics, as it can lead to better strategies and decisions by the players and coaching staff. The objective of this paper is to automatically recognize basketball group activities from tracking data representing locations of players and the ball during a game. We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS. To efficiently model the player relations in team sports, we combined a Transformer-based architecture with LSTM embedding, and a team-wise pooling layer to recognize the group activity. Training such a neural network generally requires a large amount of annotated data, which incurs high labeling cost. To address scarcity of manual labels, we generate weak-labels and pretrain the neural network on a self-supervised trajectory prediction task. We used a large tracking data set from 632 NBA games to evaluate our approach. The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy. | ['Slobodan Vucetic', 'Sandro Hauri'] | 2022-08-31 | null | null | null | null | ['sports-analytics', 'group-activity-recognition'] | ['computer-vision', 'computer-vision'] | [-4.53757271e-02 -3.52366865e-01 -3.74403775e-01 -2.56568342e-01
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a5c0ac02-ca00-4743-b327-01d193ef58f5 | one-embedder-any-task-instruction-finetuned | 2212.09741 | null | https://arxiv.org/abs/2212.09741v3 | https://arxiv.org/pdf/2212.09741v3.pdf | One Embedder, Any Task: Instruction-Finetuned Text Embeddings | We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io. | ['Weijia Shi', 'Tao Yu', 'Luke Zettlemoyer', 'Noah A. Smith', 'Wen-tau Yih', 'Mari Ostendorf', 'Yushi Hu', 'Yizhong Wang', 'Jungo Kasai', 'Hongjin Su'] | 2022-12-19 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 2.29653820e-01 4.11004983e-02 -4.08290327e-01 -6.03932917e-01
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c1f4e705-abe4-4f81-ac0a-f4f8a2d7146b | refinenet-multi-path-refinement-networks-for | 1611.06612 | null | http://arxiv.org/abs/1611.06612v3 | http://arxiv.org/pdf/1611.06612v3.pdf | RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation | Recently, very deep convolutional neural networks (CNNs) have shown
outstanding performance in object recognition and have also been the first
choice for dense classification problems such as semantic segmentation.
However, repeated subsampling operations like pooling or convolution striding
in deep CNNs lead to a significant decrease in the initial image resolution.
Here, we present RefineNet, a generic multi-path refinement network that
explicitly exploits all the information available along the down-sampling
process to enable high-resolution prediction using long-range residual
connections. In this way, the deeper layers that capture high-level semantic
features can be directly refined using fine-grained features from earlier
convolutions. The individual components of RefineNet employ residual
connections following the identity mapping mindset, which allows for effective
end-to-end training. Further, we introduce chained residual pooling, which
captures rich background context in an efficient manner. We carry out
comprehensive experiments and set new state-of-the-art results on seven public
datasets. In particular, we achieve an intersection-over-union score of 83.4 on
the challenging PASCAL VOC 2012 dataset, which is the best reported result to
date. | ['Chunhua Shen', 'Anton Milan', 'Guosheng Lin', 'Ian Reid'] | 2016-11-20 | refinenet-multi-path-refinement-networks-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [ 0.47524485 -0.03379475 -0.11205299 -0.6062903 -0.65347725 -0.24205014
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739e7050-5416-48f1-933e-ba5f2ca7f848 | mucic-at-comma-icon-multilingual-gender | null | null | https://aclanthology.org/2021.icon-multigen.9 | https://aclanthology.org/2021.icon-multigen.9.pdf | MUCIC at ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Using N-grams and Multilingual Sentence Encoders | Social media analytics are widely being explored by researchers for various applications. Prominent among them are identifying and blocking abusive contents especially targeting individuals and communities, for various reasons. The increasing abusive contents and the increasing number of users on social media demands automated tools to detect and filter the abusive contents as it is highly impossible to handle this manually. To address the challenges of detecting abusive contents, this paper describes the approaches proposed by our team MUCIC for Multilingual Gender Biased and Communal Language Identification shared task (ComMA@ICON) at International Conference on Natural Language Processing (ICON) 2021. This shared task dataset consists of code-mixed multi-script texts in Meitei, Bangla, Hindi as well as in Multilingual (a combination of Meitei, Bangla, Hindi, and English). The shared task is modeled as a multi-label Text Classification (TC) task combining word and char n-grams with vectors obtained from Multilingual Sentence Encoder (MSE) to train the Machine Learning (ML) classifiers using Pre-aggregation and Post-aggregation of labels. These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0.350, 0.412, and 0.380 respectively using Pre-aggregation of labels. | ['Alexander Gelbukh', 'Grigori Sidorov', 'Hosahalli Lakshmaiah Shashirekha', 'Oxana Vitman', 'Fazlourrahman Balouchzahi'] | null | null | null | null | icon-2021-12 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-2.17138425e-01 -1.89355180e-01 6.84121177e-02 -4.17132616e-01
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0c470ad7-085e-451a-a998-bbd420a30aee | guiding-safe-exploration-with-weakest | 2209.14148 | null | https://arxiv.org/abs/2209.14148v2 | https://arxiv.org/pdf/2209.14148v2.pdf | Guiding Safe Exploration with Weakest Preconditions | In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions. | ['Isil Dillig', 'Swarat Chaudhuri', 'Greg Anderson'] | 2022-09-28 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-5.77890361e-03 2.14773268e-01 -6.11116827e-01 8.69785845e-02
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b8461134-345d-4cdf-ae86-37bfded00e30 | sg-lstm-social-group-lstm-for-robot | 2303.04320 | null | https://arxiv.org/abs/2303.04320v1 | https://arxiv.org/pdf/2303.04320v1.pdf | SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds | With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact robots may have on humans and follow unwritten social norms in these situations. Our research aims to develop a model that can predict the movements of pedestrians and perceptually-social groups in crowded environments. We introduce a new Social Group Long Short-term Memory (SG-LSTM) model that models human groups and interactions in dense environments using a socially-aware LSTM to produce more accurate trajectory predictions. Our approach enables navigation algorithms to calculate collision-free paths faster and more accurately in crowded environments. Additionally, we also release a large video dataset with labeled pedestrian groups for the broader social navigation community. We show comparisons with different metrics on different datasets (ETH, Hotel, MOT15) and different prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime performance. | ['Aniket Bera', 'Maurice Chiu', 'Rashmi Bhaskara'] | 2023-03-08 | null | null | null | null | ['social-navigation', 'robot-navigation'] | ['robots', 'robots'] | [-1.84889704e-01 3.26068819e-01 3.08033288e-01 -3.36944252e-01
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709a4d2f-3d97-4b47-90b8-9456a7b04602 | object-counting-from-aerial-remote-sensing | 2306.10439 | null | https://arxiv.org/abs/2306.10439v1 | https://arxiv.org/pdf/2306.10439v1.pdf | Object counting from aerial remote sensing images: application to wildlife and marine mammals | Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques to automate counting tasks. Inspired by previous studies on crowd and animal counting, a UNet model with various backbones is implemented, which uses Gaussian density maps for training, bypassing the need of training a detector. The new model is applied to the task of counting dolphins and elephants in aerial images. Quantitative evaluation shows promising results, with the EfficientNet-B5 backbone achieving the best performance for African elephants and the ResNet18 backbone for dolphins. The model accurately locates animals despite complex image background conditions. By leveraging artificial intelligence, this research contributes to wildlife conservation efforts and enhances coexistence between humans and wildlife through efficient object counting without detection from aerial remote sensing. | ['Minh-Tan Pham', 'Hugo Gangloff', 'Tanya Singh'] | 2023-06-17 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-1.76736325e-01 -2.87579119e-01 2.36095980e-01 -9.44717452e-02
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39780be2-518e-4843-a9eb-526721e2bfa6 | epigraf-rethinking-training-of-3d-gans | 2206.10535 | null | https://arxiv.org/abs/2206.10535v2 | https://arxiv.org/pdf/2206.10535v2.pdf | EpiGRAF: Rethinking training of 3D GANs | A very recent trend in generative modeling is building 3D-aware generators from 2D image collections. To induce the 3D bias, such models typically rely on volumetric rendering, which is expensive to employ at high resolutions. During the past months, there appeared more than 10 works that address this scaling issue by training a separate 2D decoder to upsample a low-resolution image (or a feature tensor) produced from a pure 3D generator. But this solution comes at a cost: not only does it break multi-view consistency (i.e. shape and texture change when the camera moves), but it also learns the geometry in a low fidelity. In this work, we show that it is possible to obtain a high-resolution 3D generator with SotA image quality by following a completely different route of simply training the model patch-wise. We revisit and improve this optimization scheme in two ways. First, we design a location- and scale-aware discriminator to work on patches of different proportions and spatial positions. Second, we modify the patch sampling strategy based on an annealed beta distribution to stabilize training and accelerate the convergence. The resulted model, named EpiGRAF, is an efficient, high-resolution, pure 3D generator, and we test it on four datasets (two introduced in this work) at $256^2$ and $512^2$ resolutions. It obtains state-of-the-art image quality, high-fidelity geometry and trains ${\approx} 2.5 \times$ faster than the upsampler-based counterparts. Project website: https://universome.github.io/epigraf. | ['Peter Wonka', 'Yiqun Wang', 'Sergey Tulyakov', 'Ivan Skorokhodov'] | 2022-06-21 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.28454992e-01 4.46740761e-02 2.81487018e-01 -2.13600874e-01
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2c22a87f-6e4b-4c91-b2e3-479007bb3bf0 | semi-supervised-3d-shape-segmentation-with | 2204.08824 | null | https://arxiv.org/abs/2204.08824v2 | https://arxiv.org/pdf/2204.08824v2.pdf | Semi-supervised 3D shape segmentation with multilevel consistency and part substitution | The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data. For the unlabeled data, we present a novel multilevel consistency loss to enforce consistency of network predictions between perturbed copies of a 3D shape at multiple levels: point-level, part-level, and hierarchical level. For the labeled data, we develop a simple yet effective part substitution scheme to augment the labeled 3D shapes with more structural variations to enhance training. Our method has been extensively validated on the task of 3D object semantic segmentation on PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It exhibits superior performance to existing semi-supervised and unsupervised pre-training 3D approaches. Our code and trained models are publicly available at https://github.com/isunchy/semi_supervised_3d_segmentation. | ['Heung-Yeung Shum', 'Yang Liu', 'Xin Tong', 'Peng-Shuai Wang', 'Hao-Xiang Guo', 'Yu-Qi Yang', 'Chun-Yu Sun'] | 2022-04-19 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.93484833e-02 5.49364328e-01 -3.13532084e-01 -9.12221372e-01
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6b54e5a6-3051-4e02-b616-d3de704983d1 | disambiguated-attention-embedding-for-multi | 2305.16912 | null | https://arxiv.org/abs/2305.16912v1 | https://arxiv.org/pdf/2305.16912v1.pdf | Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning | In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against other well-established MIPL and partial-label learning methods. Our code and datasets will be made publicly available. | ['Min-Ling Zhang', 'Weijia Zhang', 'Wei Tang'] | 2023-05-26 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.28532541e-01 1.59688994e-01 -7.16818929e-01 -5.57278454e-01
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c005433b-683e-44c8-a1e1-b2cb55c219fb | semi-supervised-object-detection-via-virtual-1 | 2207.03433 | null | https://arxiv.org/abs/2207.03433v2 | https://arxiv.org/pdf/2207.03433v2.pdf | Semi-supervised Object Detection via Virtual Category Learning | Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the confirmation bias issue caused by inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a virtual category (VC) is assigned to each confusing sample such that they can safely contribute to the model optimisation even without a concrete label. It is attributed to specifying the embedding distance between the training sample and the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels. | ['Jungong Han', 'Kurt Debattista', 'Changrui Chen'] | 2022-07-07 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 3.81898522e-01 4.06675935e-01 -3.95571798e-01 -6.07262969e-01
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a51ded75-d334-4d68-9351-28931ec7591e | deep-image-compression-using-scene-text | 2305.11373 | null | https://arxiv.org/abs/2305.11373v1 | https://arxiv.org/pdf/2305.11373v1.pdf | Deep Image Compression Using Scene Text Quality Assessment | Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In this paper, we propose an image compression method for maintaining text quality. We developed a scene text image quality assessment model to assess text quality in compressed images. The assessment model iteratively searches for the best-compressed image holding high-quality text. Objective and subjective results showed that the proposed method was superior to existing methods. Furthermore, the proposed assessment model outperformed other deep-learning regression models. | ['Shinichiro Omachi', 'Tomo Miyazaki', 'Shohei Uchigasaki'] | 2023-05-19 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 4.28564698e-01 -6.07844353e-01 -2.91967005e-01 -3.52265626e-01
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7ae56d8c-5782-4e74-a4d3-a513757bbd6f | novel-feature-extraction-selection-and-fusion | 1511.04317 | null | http://arxiv.org/abs/1511.04317v2 | http://arxiv.org/pdf/1511.04317v2.pdf | Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification | Modern malware is designed with mutation characteristics, namely polymorphism
and metamorphism, which causes an enormous growth in the number of variants of
malware samples. Categorization of malware samples on the basis of their
behaviors is essential for the computer security community, because they
receive huge number of malware everyday, and the signature extraction process
is usually based on malicious parts characterizing malware families. Microsoft
released a malware classification challenge in 2015 with a huge dataset of near
0.5 terabytes of data, containing more than 20K malware samples. The analysis
of this dataset inspired the development of a novel paradigm that is effective
in categorizing malware variants into their actual family groups. This paradigm
is presented and discussed in the present paper, where emphasis has been given
to the phases related to the extraction, and selection of a set of novel
features for the effective representation of malware samples. Features can be
grouped according to different characteristics of malware behavior, and their
fusion is performed according to a per-class weighting paradigm. The proposed
method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware
Challenge dataset. | ['Giorgio Giacinto', 'Stanislav Semenov', 'Mansour Ahmadi', 'Dmitry Ulyanov', 'Mikhail Trofimov'] | 2015-11-13 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.40795052e-01 -7.08082557e-01 -1.21010661e-01 -2.45871142e-01
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986455c3-29dd-413e-ae82-160f24baf6f3 | light-field-super-resolution-via-graph-based | 1701.02141 | null | http://arxiv.org/abs/1701.02141v2 | http://arxiv.org/pdf/1701.02141v2.pdf | Light Field Super-Resolution Via Graph-Based Regularization | Light field cameras capture the 3D information in a scene with a single
exposure. This special feature makes light field cameras very appealing for a
variety of applications: from post-capture refocus, to depth estimation and
image-based rendering. However, light field cameras suffer by design from
strong limitations in their spatial resolution, which should therefore be
augmented by computational methods. On the one hand, off-the-shelf single-frame
and multi-frame super-resolution algorithms are not ideal for light field data,
as they do not consider its particular structure. On the other hand, the few
super-resolution algorithms explicitly tailored for light field data exhibit
significant limitations, such as the need to estimate an explicit disparity map
at each view. In this work we propose a new light field super-resolution
algorithm meant to address these limitations. We adopt a multi-frame alike
super-resolution approach, where the complementary information in the different
light field views is used to augment the spatial resolution of the whole light
field. We show that coupling the multi-frame approach with a graph regularizer,
that enforces the light field structure via nonlocal self similarities, permits
to avoid the costly and challenging disparity estimation step for all the
views. Extensive experiments show that the new algorithm compares favorably to
the other state-of-the-art methods for light field super-resolution, both in
terms of PSNR and visual quality. | ['Pascal Frossard', 'Mattia Rossi'] | 2017-01-09 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 5.39435983e-01 -3.60020489e-01 1.29272789e-01 -2.72017986e-01
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345407e9-0491-4333-bf26-3d37e7475e92 | enhancing-diversity-of-defocus-blur-detectors | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Enhancing_Diversity_of_Defocus_Blur_Detectors_via_Cross-Ensemble_Network_CVPR_2019_paper.pdf | Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network | Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress through exploring deeper or wider networks with the expense of high memory and computation. In this paper, we propose a novel learning strategy by breaking DBD problem into multiple smaller defocus blur detectors and thus estimate errors can cancel out each other. Our focus is the diversity enhancement via cross-ensemble network. Specifically, we design an end-to-end network composed of two logical parts: feature extractor network (FENet) and defocus blur detector cross-ensemble network (DBD-CENet). FENet is constructed to extract low-level features. Then the features are fed into DBD-CENet containing two parallel-branches for learning two groups of defocus blur detectors. For each individual, we design cross-negative and self-negative correlations and an error function to enhance ensemble diversity and balance individual accuracy. Finally, the multiple defocus blur detectors are combined with a uniformly weighted average to obtain the final DBD map. Experimental results indicate the superiority of our method in terms of accuracy and speed when compared with several state-of-the-art methods.
| [' Huchuan Lu', ' Qiuhua Lin', ' Bowen Zheng', 'Wenda Zhao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['defocus-blur-detection', 'defocus-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.42250499e-02 -7.65254736e-01 2.34261051e-01 -4.66463536e-01
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73d7cf1c-f0c3-466a-94d2-4bf29c682420 | ogmn-occlusion-guided-multi-task-network-for | 2304.11805 | null | https://arxiv.org/abs/2304.11805v1 | https://arxiv.org/pdf/2304.11805v1.pdf | OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images | Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation characteristic. And we found that extracting or localizing occlusion between objects is beneficial for the detector to address this challenge. According to this finding, the occlusion localization task is introduced, which together with the object detection task constitutes our occlusion-guided multi-task network (OGMN). The OGMN contains the localization of occlusion and two occlusion-guided multi-task interactions. In detail, an occlusion estimation module (OEM) is proposed to precisely localize occlusion. Then the OGMN utilizes the occlusion localization results to implement occlusion-guided detection with two multi-task interactions. One interaction for the guide is between two task decoders to address the feature confusion problem, and an occlusion decoupling head (ODH) is proposed to replace the general detection head. Another interaction for guide is designed in the detection process according to local aggregation characteristic, and a two-phase progressive refinement process (TPP) is proposed to optimize the detection process. Extensive experiments demonstrate the effectiveness of our OGMN on the Visdrone and UAVDT datasets. In particular, our OGMN achieves 35.0% mAP on the Visdrone dataset and outperforms the baseline by 5.3%. And our OGMN provides a new insight for accurate occlusion localization and achieves competitive detection performance. | ['Xian Sun', 'Xinming Li', 'Xiuhua Mao', 'Peng Gao', 'Yongqiang Mao', 'Wenhui Diao', 'Xuexue Li'] | 2023-04-24 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 6.60497546e-02 -3.46382290e-01 3.02184410e-02 -1.55357853e-01
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9038e6af-f0d9-4c92-bf21-339a21d72350 | multimodal-contrastive-learning-via-uni-modal | 2210.14556 | null | https://arxiv.org/abs/2210.14556v1 | https://arxiv.org/pdf/2210.14556v1.pdf | Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis | Multimodal representation learning is a challenging task in which previous work mostly focus on either uni-modality pre-training or cross-modality fusion. In fact, we regard modeling multimodal representation as building a skyscraper, where laying stable foundation and designing the main structure are equally essential. The former is like encoding robust uni-modal representation while the later is like integrating interactive information among different modalities, both of which are critical to learning an effective multimodal representation. Recently, contrastive learning has been successfully applied in representation learning, which can be utilized as the pillar of the skyscraper and benefit the model to extract the most important features contained in the multimodal data. In this paper, we propose a novel framework named MultiModal Contrastive Learning (MMCL) for multimodal representation to capture intra- and inter-modality dynamics simultaneously. Specifically, we devise uni-modal contrastive coding with an efficient uni-modal feature augmentation strategy to filter inherent noise contained in acoustic and visual modality and acquire more robust uni-modality representations. Besides, a pseudo siamese network is presented to predict representation across different modalities, which successfully captures cross-modal dynamics. Moreover, we design two contrastive learning tasks, instance- and sentiment-based contrastive learning, to promote the process of prediction and learn more interactive information related to sentiment. Extensive experiments conducted on two public datasets demonstrate that our method surpasses the state-of-the-art methods. | ['Haifeng Hu', 'Ronghao Lin'] | 2022-10-26 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 3.35140795e-01 -5.88883281e-01 -3.53016973e-01 -1.31137609e-01
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ac024f56-dc5e-4a59-816f-f90240938630 | joint-background-reconstruction-and | 1707.07584 | null | http://arxiv.org/abs/1707.07584v1 | http://arxiv.org/pdf/1707.07584v1.pdf | Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network | Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework for foreground segmentation in video sequences. In the first
stage, a convolutional encoder-decoder sub-network is employed to reconstruct
the background images and encode rich prior knowledge of background scenes. In
the second stage, the reconstructed background and current frame are input into
a multi-channel fully-convolutional sub-network (MCFCN) for accurate foreground
segmentation. In the two-stage CNN, the reconstruction loss and segmentation
loss are jointly optimized. The background images and foreground objects are
output simultaneously in an end-to-end way. Moreover, by incorporating the
prior semantic knowledge of foreground and background in the pre-training
process, our method could restrain the background noise and keep the integrity
of foreground objects at the same time. Experiments on CDNet 2014 show that our
method outperforms the state-of-the-art by 4.9%. | ['Yingying Chen', 'Jinqiao Wang', 'Xu Zhao', 'Ming Tang'] | 2017-07-24 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.18054128e-01 -5.96016832e-02 3.02208979e-02 -3.41727644e-01
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8f951009-7a4d-4942-8bb5-06a73ace765c | secure-and-efficient-flexibility-service | 2306.17475 | null | https://arxiv.org/abs/2306.17475v1 | https://arxiv.org/pdf/2306.17475v1.pdf | Secure and Efficient Flexibility Service Procurement: A Game-Theoretic Approach | Procuring flexibility services from energy consumers has been a potential solution to accommodating renewable generations in future power system. However, efficiently and securely coordinating the behaviors of diverse market participants within a privacy-preserving environment remains a challenge. This paper addresses this issue by introducing a game-theoretic market framework for real-time energy balancing. The competition among energy consumers is modeled as a Generalized Nash Game (GNG), which enables the analysis of their strategic decision-making. To mitigate the market power exerted by active energy consumers, we employ a supply function-based bidding method in this market design. We incorporate physical constraints to ensure the secure operation of the distribution network. Previous approaches to steering consumers towards the Generalized Nash Equilibrium (GNE) of this game often necessitate the sharing of private information, either in full or in part, which may not be practically feasible. To overcome this limitation, we propose a preconditioned forward-backward algorithm, with analytical convergence guarantees, that only requires participants to share limited, non-private sensitive information with others. Finally, numerical simulations on the enhanced IEEE 33-bus test case validate the effectiveness of our proposed market mechanism and algorithm. | ['Nima Monshizadeh', 'Jacquelien M. A. Scherpen', 'Koorosh Shomalzadeh', 'Xiupeng Chen'] | 2023-06-30 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-4.82545286e-01 1.04724281e-01 -1.13145567e-01 -1.55098304e-01
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ca412f04-f49e-4e6a-8a9e-713873b3db5e | toward-better-target-representation-for | 2208.10531 | null | https://arxiv.org/abs/2208.10531v3 | https://arxiv.org/pdf/2208.10531v3.pdf | RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation | Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation. | ['Chen Chen', 'Lichao Sun', 'Lingjuan Lyu', 'Zhengming Ding', 'Qucheng Peng'] | 2022-08-22 | null | null | null | null | ['self-knowledge-distillation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.18562180e-01 2.68778712e-01 -4.54333395e-01 -4.51364905e-01
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9740b426-09c5-4a32-8006-c2622f0f9129 | camera-calibration-and-player-localization-in | 2104.09333 | null | https://arxiv.org/abs/2104.09333v1 | https://arxiv.org/pdf/2104.09333v1.pdf | Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting | Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances. | ['Marc Van Droogenbroeck', 'Bernard Ghanem', 'Olivier Barnich', 'Silvio Giancola', 'Floriane Magera', 'Adrien Deliège', 'Anthony Cioppa'] | 2021-04-19 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [-7.76606351e-02 -3.07870060e-01 -2.25186154e-01 -2.15871125e-01
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fb50ce28-1814-4ce0-b70f-b77a3d3249ea | deep-learning-based-joint-control-of-acoustic | 2203.01793 | null | https://arxiv.org/abs/2203.01793v2 | https://arxiv.org/pdf/2203.01793v2.pdf | Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering | We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF). While the AEC removes the early echo component, the MVDR BF and PF suppress the residual echo and background noise. As key innovation, we suggest to use a single deep neural network (DNN) to jointly control the adaptation of the various algorithmic components. This allows for rapid convergence and high steady-state performance in the presence of high-level interfering double-talk. End-to-end training of the DNN using a time-domain speech extraction loss function avoids the design of individual control strategies. | ['Walter Kellermann', 'Thomas Haubner'] | 2022-03-03 | null | null | null | null | ['acoustic-echo-cancellation', 'speech-extraction', 'acoustic-echo-cancellation'] | ['medical', 'speech', 'speech'] | [ 1.09282747e-01 1.53607838e-02 6.48695827e-01 -6.00418486e-02
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d3d9471a-e973-4e95-9a1e-0af40d0d3cc5 | a-riemannian-framework-for-matching-point | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Deng_A_Riemannian_Framework_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Deng_A_Riemannian_Framework_2014_CVPR_paper.pdf | A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform | In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation. This is achieved by solving a static Schrodinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit L_2 norm---making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm. We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques. Finally, to evaluate the performance of our algorithm---dubbed SDTM---we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques. The experiments show that our algorithm outperforms state-of the-art point set registration algorithms on many quantitative metrics. | ['Baba C. Vemuri', 'Yan Deng', 'Anand Rangarajan', 'Stephan Eisenschenk'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['set-matching'] | ['computer-vision'] | [ 6.25301152e-02 8.76841918e-02 1.46014810e-01 -2.97857583e-01
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46f03fb2-9557-4555-9483-e1d414bc87c7 | a-self-attentive-model-for-knowledge-tracing | 1907.06837 | null | https://arxiv.org/abs/1907.06837v1 | https://arxiv.org/pdf/1907.06837v1.pdf | A Self-Attentive model for Knowledge Tracing | Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average. | ['George Karypis', 'Shalini Pandey'] | 2019-07-16 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.42042907e-03 -9.33963284e-02 -6.58634722e-01 -2.04217628e-01
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c8a8f80c-27bd-4d8e-8971-b609414849ab | dense-event-ordering-with-a-multi-pass | null | null | https://aclanthology.org/Q14-1022 | https://aclanthology.org/Q14-1022.pdf | Dense Event Ordering with a Multi-Pass Architecture | The past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset of the possible ordering graph. Many evaluations follow suit by only testing certain pairs of events (e.g., only main verbs of neighboring sentences). This has led most research to focus on specific learners for partial labelings. This paper attempts to nudge the discussion from identifying some relations to all relations. We present new experiments on strongly connected event graphs that contain ∼10 times more relations per document than the TimeBank. We also describe a shift away from the single learner to a sieve-based architecture that naturally blends multiple learners into a precision-ranked cascade of sieves. Each sieve adds labels to the event graph one at a time, and earlier sieves inform later ones through transitive closure. This paper thus describes innovations in both approach and task. We experiment on the densest event graphs to date and show a 14{\%} gain over state-of-the-art. | ['Taylor Cassidy', 'Nathanael Chambers', 'Bill McDowell', 'Steven Bethard'] | 2014-01-01 | null | null | null | tacl-2014-1 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-5.77989072e-02 6.72426820e-01 -4.70443994e-01 -7.51641512e-01
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d739129a-fa7b-4aef-893c-b56ee52f6c51 | on-the-stability-of-low-pass-graph-filter | 2110.07234 | null | https://arxiv.org/abs/2110.07234v1 | https://arxiv.org/pdf/2110.07234v1.pdf | On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires | Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for GCNs. Many existing results have focused on the regime of small perturbation with a small number of edge rewires. However, the number of edge rewires can be large in many applications. To study the latter case, this work departs from the previous analysis and proves a bound on the stability of graph filter relying on the filter's frequency response. Assuming the graph filter is low pass, we show that the stability of the filter depends on perturbation to the community structure. As an application, we show that for stochastic block model graphs, the graph filter distance converges to zero when the number of nodes approaches infinity. Numerical simulations validate our findings. | ['Hoi-To Wai', 'Yiran He', 'Hoang-Son Nguyen'] | 2021-10-14 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.59214770e-02 4.59486008e-01 1.43844873e-01 2.76688248e-01
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01e06066-2a0f-47d7-9ccf-600a8ad32465 | time-series-kernel-similarities-for | 1801.06845 | null | http://arxiv.org/abs/1801.06845v2 | http://arxiv.org/pdf/1801.06845v2.pdf | Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs | We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance. | ['Filippo Maria Bianchi', 'Miroslaw Malek', 'Jelena Milosevic', 'Alberto Ferrante', 'Lorenzo Livi'] | 2018-01-21 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.35481882e-01 -3.93297940e-01 1.50782436e-01 -5.48061207e-02
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37aa221a-f468-459c-b85e-2742c09da98a | deep-partial-multi-view-learning | 2011.06170 | null | https://arxiv.org/abs/2011.06170v1 | https://arxiv.org/pdf/2011.06170v1.pdf | Deep Partial Multi-View Learning | Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and flflexibly take advantage of multiple partial views. We fifirst provide a formal defifinition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the learned latent representations. For completeness, the task of learning latent multi-view representation is specififically translated to a degradation process by mimicking data transmission, such that the optimal tradeoff between consistency and complementarity across different views can be implicitly achieved. Equipped with adversarial strategy, our model stably imputes missing views, encoding information from all views for each sample to be encoded into latent representation to further enhance the completeness. Furthermore, a nonparametric classifification loss is introduced to produce structured representations and prevent overfifitting, which endows the algorithm with promising generalization under view-missing cases. Extensive experimental results validate the effectiveness of our algorithm over existing state of the arts for classifification, representation learning and data imputation. | ['QinGhua Hu', 'Huazhu Fu', 'Joey Tianyi Zhou', 'Zongbo Han', 'Yajie Cui', 'Changqing Zhang'] | 2020-11-12 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.86926627e-01 2.10399568e-01 -6.89310968e-01 -3.71597588e-01
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494fcbc5-9fc6-424e-abe5-8633b363f4a7 | atlas-end-to-end-3d-scene-reconstruction-from | 2003.10432 | null | https://arxiv.org/abs/2003.10432v3 | https://arxiv.org/pdf/2003.10432v3.pdf | Atlas: End-to-End 3D Scene Reconstruction from Posed Images | We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene. We hypothesize that a direct regression to 3D is more effective. A 2D CNN extracts features from each image independently which are then back-projected and accumulated into a voxel volume using the camera intrinsics and extrinsics. After accumulation, a 3D CNN refines the accumulated features and predicts the TSDF values. Additionally, semantic segmentation of the 3D model is obtained without significant computation. This approach is evaluated on the Scannet dataset where we significantly outperform state-of-the-art baselines (deep multiview stereo followed by traditional TSDF fusion) both quantitatively and qualitatively. We compare our 3D semantic segmentation to prior methods that use a depth sensor since no previous work attempts the problem with only RGB input. | ['Zak Murez', 'James Bartolozzi', 'Ayan Sinha', 'Vijay Badrinarayanan', 'Tarrence van As', 'Andrew Rabinovich'] | 2020-03-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/277_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520409.pdf | eccv-2020-8 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.88643849e-01 3.80492240e-01 2.19670072e-01 -9.39621568e-01
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f5c6270b-4e70-481d-86c7-e5dea85076de | frustum-pointnets-for-3d-object-detection | 1711.08488 | null | http://arxiv.org/abs/1711.08488v2 | http://arxiv.org/pdf/1711.08488v2.pdf | Frustum PointNets for 3D Object Detection from RGB-D Data | In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability. | ['Wei Liu', 'Leonidas J. Guibas', 'Chenxia Wu', 'Hao Su', 'Charles R. Qi'] | 2017-11-22 | frustum-pointnets-for-3d-object-detection-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Qi_Frustum_PointNets_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [-3.40012312e-02 -1.37952968e-01 9.96765494e-02 -1.48858875e-01
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7d51dd4c-6c16-4d31-9e72-0de3b9f2ceac | evaluating-impact-of-user-cluster-targeted | 2305.04694 | null | https://arxiv.org/abs/2305.04694v1 | https://arxiv.org/pdf/2305.04694v1.pdf | Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders | In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences. In this work, we conduct a systematic study on user-cluster targeted data poisoning attacks on Matrix Factorisation (MF) based RS, where an adversary injects fake users with falsely crafted user-item feedback to promote an item to a specific user cluster. We analyse how user and item feature matrices change after data poisoning attacks and identify the factors that influence the effectiveness of the attack on these feature matrices. We demonstrate that the adversary can easily target specific user clusters with minimal effort and that some items are more susceptible to attacks than others. Our theoretical analysis has been validated by the experimental results obtained from two real-world datasets. Our observations from the study could serve as a motivating point to design a more robust RS. | ['Douglas Leith', 'Sulthana Shams'] | 2023-05-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.78099510e-02 -2.75645763e-01 -2.94691175e-01 2.19660047e-02
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39ee0e96-0fc7-4b8a-baec-101b965fc1f2 | unbiased-scene-graph-generation-in-videos | 2304.00733 | null | https://arxiv.org/abs/2304.00733v3 | https://arxiv.org/pdf/2304.00733v3.pdf | Unbiased Scene Graph Generation in Videos | The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG. Existing methods for dynamic SGG have primarily focused on capturing spatio-temporal context using complex architectures without addressing the challenges mentioned above, especially the long-tailed distribution of relationships. This often leads to the generation of biased scene graphs. To address these challenges, we introduce a new framework called TEMPURA: TEmporal consistency and Memory Prototype guided UnceRtainty Attenuation for unbiased dynamic SGG. TEMPURA employs object-level temporal consistencies via transformer-based sequence modeling, learns to synthesize unbiased relationship representations using memory-guided training, and attenuates the predictive uncertainty of visual relations using a Gaussian Mixture Model (GMM). Extensive experiments demonstrate that our method achieves significant (up to 10% in some cases) performance gain over existing methods highlighting its superiority in generating more unbiased scene graphs. | ['Amit K. Roy Chowdhury', 'Subarna Tripathi', 'Kyle Min', 'Sayak Nag'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nag_Unbiased_Scene_Graph_Generation_in_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 3.68889481e-01 3.26930322e-02 1.27087161e-02 -2.83650666e-01
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3298dd76-fce5-4167-80c1-bd5555b2de37 | dual-self-distillation-of-u-shaped-networks | 2306.03271 | null | https://arxiv.org/abs/2306.03271v1 | https://arxiv.org/pdf/2306.03271v1.pdf | Dual self-distillation of U-shaped networks for 3D medical image segmentation | U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework for U-shaped networks for 3D medical image segmentation. DSD distills knowledge from the ground-truth segmentation labels to the decoder layers and also between the encoder and decoder layers of a single U-shaped network. DSD is a generalized training strategy that could be attached to the backbone architecture of any U-shaped network to further improve its segmentation performance. We attached DSD on two state-of-the-art U-shaped backbones, and extensive experiments on two public 3D medical image segmentation datasets (cardiac substructure and brain tumor) demonstrated significant improvement over those backbones. On average, after attaching DSD to the U-shaped backbones, we observed an improvement of 4.25% and 3.15% in Dice similarity score for cardiac substructure and brain tumor segmentation respectively. | ['Carri Glide-Hurst', 'Ming Dong', 'Soumyanil Banerjee'] | 2023-06-05 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.52328968e-01 8.12185049e-01 -1.37142986e-01 -5.70201933e-01
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5a85ffe9-8c0f-48c4-b9f3-b8a25dc9b6bb | continuous-spectral-reconstruction-from-rgb | 2112.13003 | null | https://arxiv.org/abs/2112.13003v2 | https://arxiv.org/pdf/2112.13003v2.pdf | Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation | Existing methods for spectral reconstruction usually learn a discrete mapping from RGB images to a number of spectral bands. However, this modeling strategy ignores the continuous nature of spectral signature. In this paper, we propose Neural Spectral Reconstruction (NeSR) to lift this limitation, by introducing a novel continuous spectral representation. To this end, we embrace the concept of implicit function and implement a parameterized embodiment with a neural network. Specifically, we first adopt a backbone network to extract spatial features of RGB inputs. Based on it, we devise Spectral Profile Interpolation (SPI) module and Neural Attention Mapping (NAM) module to enrich deep features, where the spatial-spectral correlation is involved for a better representation. Then, we view the number of sampled spectral bands as the coordinate of continuous implicit function, so as to learn the projection from deep features to spectral intensities. Extensive experiments demonstrate the distinct advantage of NeSR in reconstruction accuracy over baseline methods. Moreover, NeSR extends the flexibility of spectral reconstruction by enabling an arbitrary number of spectral bands as the target output. | ['Zhiwei Xiong', 'Lizhi Wang', 'Chang Chen', 'Mingde Yao', 'Ruikang Xu'] | 2021-12-24 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.15290940e-01 -3.35500151e-01 -1.73836157e-01 -5.51519156e-01
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87fd8b28-f6a0-4abe-a835-09ac618e4aea | mug-multi-human-graph-network-for-3d-mesh | 2205.12583 | null | https://arxiv.org/abs/2205.12583v2 | https://arxiv.org/pdf/2205.12583v2.pdf | MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose | Reconstructing multi-human body mesh from a single monocular image is an important but challenging computer vision problem. In addition to the individual body mesh models, we need to estimate relative 3D positions among subjects to generate a coherent representation. In this work, through a single graph neural network, named MUG (Multi-hUman Graph network), we construct coherent multi-human meshes using only multi-human 2D pose as input. Compared with existing methods, which adopt a detection-style pipeline (i.e., extracting image features and then locating human instances and recovering body meshes from that) and suffer from the significant domain gap between lab-collected training datasets and in-the-wild testing datasets, our method benefits from the 2D pose which has a relatively consistent geometric property across datasets. Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i.e., skeleton and mesh structure). Second, it employs a dual-branch graph neural network structure -- one for predicting inter-human depth relation and the other one for predicting root-joint-relative mesh coordinates. Finally, the entire multi-human 3D meshes are constructed by combining the output from both branches. Extensive experiments demonstrate that MUG outperforms previous multi-human mesh estimation methods on standard 3D human benchmarks -- Panoptic, MuPoTS-3D and 3DPW. | ['James Wang', 'Xianfeng Tang', 'Yandong Li', 'Chenyan Wu'] | 2022-05-25 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-9.72928330e-02 1.01146564e-01 1.96236111e-02 -1.38004750e-01
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d0b473a5-4b43-4e02-b176-f712196891bb | unigeo-unifying-geometry-logical-reasoning | 2212.02746 | null | https://arxiv.org/abs/2212.02746v1 | https://arxiv.org/pdf/2212.02746v1.pdf | UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression | Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multiple math tasks. However, in essence, these two tasks have similar problem representations and overlapped math knowledge which can improve the understanding and reasoning ability of a deep model on both two tasks. Therefore, we construct a large-scale Unified Geometry problem benchmark, UniGeo, which contains 4,998 calculation problems and 9,543 proving problems. Each proving problem is annotated with a multi-step proof with reasons and mathematical expressions. The proof can be easily reformulated as a proving sequence that shares the same formats with the annotated program sequence for calculation problems. Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation. Furthermore, we propose a Mathematical Expression Pretraining (MEP) method that aims to predict the mathematical expressions in the problem solution, thus improving the Geoformer model. Experiments on the UniGeo demonstrate that our proposed Geoformer obtains state-of-the-art performance by outperforming task-specific model NGS with over 5.6% and 3.2% accuracies on calculation and proving problems, respectively. | ['Xiaodan Liang', 'Chongyu Chen', 'Liang Lin', 'Pan Lu', 'Jinghui Qin', 'Tong Li', 'Jiaqi Chen'] | 2022-12-06 | null | null | null | null | ['mathematical-reasoning', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [-7.48604462e-02 2.66966922e-03 1.26597032e-01 -1.69758961e-01
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7a66892b-e4ac-4f1a-b3ec-608b8616631a | deep-neural-mel-subband-beamformer-for-in-car | 2211.12590 | null | https://arxiv.org/abs/2211.12590v2 | https://arxiv.org/pdf/2211.12590v2.pdf | Deep Neural Mel-Subband Beamformer for In-car Speech Separation | While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use. In this paper, we propose DL-based mel-subband spatio-temporal beamformer to perform speech separation in a car environment with reduced computation cost and inference time. As opposed to conventional subband (SB) approaches, our framework uses a mel-scale based subband selection strategy which ensures a fine-grained processing for lower frequencies where most speech formant structure is present, and coarse-grained processing for higher frequencies. In a recursive way, robust frame-level beamforming weights are determined for each speaker location/zone in a car from the estimated subband speech and noise covariance matrices. Furthermore, proposed framework also estimates and suppresses any echoes from the loudspeaker(s) by using the echo reference signals. We compare the performance of our proposed framework to several NB, SB, and full-band (FB) processing techniques in terms of speech quality and recognition metrics. Based on experimental evaluations on simulated and real-world recordings, we find that our proposed framework achieves better separation performance over all SB and FB approaches and achieves performance closer to NB processing techniques while requiring lower computing cost. | ['Dong Yu', 'Shi-Xiong Zhang', 'Meng Yu', 'Yong Xu', 'Vinay Kothapally'] | 2022-11-22 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 4.78837937e-02 -7.89941728e-01 1.33266985e-01 -9.35271382e-02
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0174870c-3a2d-4135-ad9d-193d8e52640c | structured-matrix-completion-with | 1504.01823 | null | http://arxiv.org/abs/1504.01823v1 | http://arxiv.org/pdf/1504.01823v1.pdf | Structured Matrix Completion with Applications to Genomic Data Integration | Matrix completion has attracted significant recent attention in many fields
including statistics, applied mathematics and electrical engineering. Current
literature on matrix completion focuses primarily on independent sampling
models under which the individual observed entries are sampled independently.
Motivated by applications in genomic data integration, we propose a new
framework of structured matrix completion (SMC) to treat structured missingness
by design. Specifically, our proposed method aims at efficient matrix recovery
when a subset of the rows and columns of an approximately low-rank matrix are
observed. We provide theoretical justification for the proposed SMC method and
derive lower bound for the estimation errors, which together establish the
optimal rate of recovery over certain classes of approximately low-rank
matrices. Simulation studies show that the method performs well in finite
sample under a variety of configurations. The method is applied to integrate
several ovarian cancer genomic studies with different extent of genomic
measurements, which enables us to construct more accurate prediction rules for
ovarian cancer survival. | ['Tianxi Cai', 'T. Tony Cai', 'Anru Zhang'] | 2015-04-08 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 6.89763904e-01 1.64382160e-01 -5.06158113e-01 -2.89344966e-01
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eb4b1262-57ee-4640-97c3-b58ffde478f9 | medical-relation-extraction-with-manifold | null | null | https://aclanthology.org/P14-1078 | https://aclanthology.org/P14-1078.pdf | Medical Relation Extraction with Manifold Models | null | ['James Fan', 'Chang Wang'] | 2014-06-01 | null | null | null | acl-2014-6 | ['medical-relation-extraction'] | ['medical'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d013a701-b933-4328-abae-028b421190e2 | badlad-a-large-multi-domain-bengali-document | 2303.05325 | null | https://arxiv.org/abs/2303.05325v3 | https://arxiv.org/pdf/2303.05325v3.pdf | BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset | While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models. | ['Md. Rezwanul Haque', 'Asif Shahriyar Sushmit', 'Ahmed Imtiaz Humayun', 'Tahsin Reasat', 'Farig Sadeque', 'Sayma Sultana Chowdhury', 'Marsia Haque Meghla', 'Akib Hasan Pavel', 'Souhardya Saha Dip', 'Shahriar Elahi Dhruvo', 'Fazle Rabbi Rakib', 'Intesur Ahmed', 'MD. Nazmuddoha Ansary', 'Syed Mobassir Hossen', 'Mahfuzur Rahman Emon', 'Md. Rakibul Hasan', 'Md. Istiak Hossain Shihab'] | 2023-03-09 | null | null | null | null | ['optical-character-recognition', 'document-layout-analysis'] | ['computer-vision', 'computer-vision'] | [ 4.99165393e-02 -4.23392296e-01 9.92318988e-02 -2.61884600e-01
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f726a8cf-1367-4c72-a876-bebed467ef32 | catalyzing-clinical-diagnostic-pipelines | 2103.14969 | null | https://arxiv.org/abs/2103.14969v2 | https://arxiv.org/pdf/2103.14969v2.pdf | Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future | Deep learning has made a remarkable impact in the field of natural image processing over the past decade. Consequently, there is a great deal of interest in replicating this success across unsolved tasks in related domains, such as medical image analysis. Core to medical image analysis is the task of semantic segmentation which enables various clinical workflows. Due to the challenges inherent in manual segmentation, many decades of research have been devoted to discovering extensible, automated, expert-level segmentation techniques. Given the groundbreaking performance demonstrated by recent neural network-based techniques, deep learning seems poised to achieve what classic methods have historically been unable. This paper will briefly overview some of the state-of-the-art (SoTA) neural network-based segmentation algorithms with a particular emphasis on the most recent architectures, comparing and contrasting the contributions and characteristics of each network topology. Using ultrasonography as a motivating example, it will also demonstrate important clinical implications of effective deep learning-based solutions, articulate challenges unique to the modality, and discuss novel approaches developed in response to those challenges, concluding with the proposal of future directions in the field. Given the generally observed ephemerality of the best deep learning approaches (i.e. the extremely quick succession of the SoTA), the main contributions of the paper are its contextualization of modern deep learning architectures with historical background and the elucidation of the current trajectory of volumetric medical image segmentation research. | ['Teofilo E. Zosa'] | 2021-03-27 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 5.48506916e-01 4.21741933e-01 -2.34657764e-01 -3.47814500e-01
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84461cab-7c59-4de9-8c7b-d1f9d2773c1e | the-second-dicova-challenge-dataset-and | 2110.01177 | null | https://arxiv.org/abs/2110.01177v3 | https://arxiv.org/pdf/2110.01177v3.pdf | The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics | The Second Diagnosis of COVID-19 using Acoustics (DiCOVA) Challenge aimed at accelerating the research in acoustics based detection of COVID-19, a topic at the intersection of acoustics, signal processing, machine learning, and healthcare. This paper presents the details of the challenge, which was an open call for researchers to analyze a dataset of audio recordings consisting of breathing, cough and speech signals. This data was collected from individuals with and without COVID-19 infection, and the task in the challenge was a two-class classification. The development set audio recordings were collected from 965 (172 COVID-19 positive) individuals, while the evaluation set contained data from 471 individuals (71 COVID-19 positive). The challenge featured four tracks, one associated with each sound category of cough, speech and breathing, and a fourth fusion track. A baseline system was also released to benchmark the participants. In this paper, we present an overview of the challenge, the rationale for the data collection and the baseline system. Further, a performance analysis for the systems submitted by the $16$ participating teams in the leaderboard is also presented. | ['Sriram Ganapathy', 'Pravin Mote', 'Debottam Dutta', 'Debarpan Bhattacharya', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma'] | 2021-10-04 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.91403258e-01 -5.07740796e-01 7.14755058e-01 -2.56590873e-01
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b17a10e5-ac70-4a37-a2d9-71e7689e6655 | knowledge-base-question-answering-via | null | null | https://aclanthology.org/D18-1242 | https://aclanthology.org/D18-1242.pdf | Knowledge Base Question Answering via Encoding of Complex Query Graphs | Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions. | ['Xusheng Luo', 'Fengli Lin', 'Kenny Zhu', 'Kangqi Luo'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.21079105e-01 2.86037832e-01 -1.25785530e-01 -2.90912628e-01
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ba1faee1-75f2-4eef-aafa-6567970039b7 | weakly-supervised-action-localization-and | 2012.09542 | null | https://arxiv.org/abs/2012.09542v3 | https://arxiv.org/pdf/2012.09542v3.pdf | Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN | 3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss seems unavoidable. To improve the visual explanations and classification in 3D CNN, we propose two approaches; i) aggregate layer-wise global to local (global-local) discrete gradients using trained 3DResNext network, and ii) implement attention gating network to improve the accuracy of the action recognition. The proposed approach intends to show the usefulness of every layer termed as global-local attention in 3D CNN via visual attribution, weakly-supervised action localization, and action recognition. Firstly, the 3DResNext is trained and applied for action classification using backpropagation concerning the maximum predicted class. The gradients and activations of every layer are then up-sampled. Later, aggregation is used to produce more nuanced attention, which points out the most critical part of the predicted class's input videos. We use contour thresholding of final attention for final localization. We evaluate spatial and temporal action localization in trimmed videos using fine-grained visual explanation via 3DCam. Experimental results show that the proposed approach produces informative visual explanations and discriminative attention. Furthermore, the action recognition via attention gating on each layer produces better classification results than the baseline model. | ['Takio Kurita', 'Muthu Subash Kavitha', 'Novanto Yudistira'] | 2020-12-17 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 0.2006249 0.1910245 -0.5464479 -0.23186015 -0.3139077 -0.08641758
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89005bb2-912c-4234-a45a-c012be15f1ce | domain-adaptation-for-inertial-measurement | 2304.06489 | null | https://arxiv.org/abs/2304.06489v1 | https://arxiv.org/pdf/2304.06489v1.pdf | Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey | Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc. However, the widespread adoption of these WHAR models is impeded by their degraded performance in the presence of data distribution heterogeneities caused by the sensor placement at different body positions, inherent biases and heterogeneities across devices, and personal and environmental diversities. Various traditional machine learning algorithms and transfer learning techniques have been proposed in the literature to address the underpinning challenges of handling such data heterogeneities. Domain adaptation is one such transfer learning techniques that has gained significant popularity in recent literature. In this paper, we survey the recent progress of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based human activity recognition area, discuss potential future directions. | ['Nirmalya Roy', 'Indrajeet Ghosh', 'Abu Zaher Md Faridee', 'Avijoy Chakma'] | 2023-04-07 | null | null | null | null | ['sports-analytics', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 2.16244519e-01 -3.31893593e-01 -7.71533549e-01 -3.39406103e-01
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a82b9aff-88a4-4604-b050-b06bf831b516 | s-textsuperscript-2-fpn-scale-ware-strip | 2206.07298 | null | https://arxiv.org/abs/2206.07298v2 | https://arxiv.org/pdf/2206.07298v2.pdf | S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation | Modern high-performance semantic segmentation methods employ a heavy backbone and dilated convolution to extract the relevant feature. Although extracting features with both contextual and semantic information is critical for the segmentation tasks, it brings a memory footprint and high computation cost for real-time applications. This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation. Specifically, we proposed a lightweight model named Scale-aware Strip Attention Guided Feature Pyramid Network (S$^2$-FPN). Our network consists of three main modules: Attention Pyramid Fusion (APF) module, Scale-aware Strip Attention Module (SSAM), and Global Feature Upsample (GFU) module. APF adopts an attention mechanisms to learn discriminative multi-scale features and help close the semantic gap between different levels. APF uses the scale-aware attention to encode global context with vertical stripping operation and models the long-range dependencies, which helps relate pixels with similar semantic label. In addition, APF employs channel-wise reweighting block (CRB) to emphasize the channel features. Finally, the decoder of S$^2$-FPN then adopts GFU, which is used to fuse features from APF and the encoder. Extensive experiments have been conducted on two challenging semantic segmentation benchmarks, which demonstrate that our approach achieves better accuracy/speed trade-off with different model settings. The proposed models have achieved a results of 76.2\%mIoU/87.3FPS, 77.4\%mIoU/67FPS, and 77.8\%mIoU/30.5FPS on Cityscapes dataset, and 69.6\%mIoU,71.0\% mIoU, and 74.2\% mIoU on Camvid dataset. The code for this work will be made available at \url{https://github.com/mohamedac29/S2-FPN | ['Xin Hong', 'Tewodros Legesse Munea', 'Chenxi Huang', 'Chenhui Yang', 'Mohammed A. M. Elhassan'] | 2022-06-15 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 3.26637119e-01 -1.19326532e-01 -1.87412411e-01 -4.60871041e-01
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36b88120-21ea-41ae-9b13-658985e781bc | tomosam-a-3d-slicer-extension-using-sam-for | 2306.08609 | null | https://arxiv.org/abs/2306.08609v1 | https://arxiv.org/pdf/2306.08609v1.pdf | TomoSAM: a 3D Slicer extension using SAM for tomography segmentation | TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process. The source code associated with this article can be found at https://github.com/fsemerar/SlicerTomoSAM | ['Joseph C. Ferguson', 'Sergio Fraile Izquierdo', 'Alexandre Quintart', 'Federico Semeraro'] | 2023-06-14 | null | null | null | null | ['zero-shot-segmentation', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.32165447e-01 -2.89190173e-01 2.07773358e-01 -4.39045697e-01
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be79cd85-c757-4114-a9cb-06ee6944b3c2 | perceptual-multi-exposure-fusion | 2210.09604 | null | https://arxiv.org/abs/2210.09604v2 | https://arxiv.org/pdf/2210.09604v2.pdf | Perceptual Multi-Exposure Fusion | As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deployed on mobile devices. This paper presents a perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We analyze the potential defects of three classical exposure measures in lieu of using detail-enhancement component and improve two of them, namely adaptive Wellexposedness (AWE) and the gradient of color images (3-D gradient). AWE designed in YCbCr color space considers the difference between varying exposure images. 3-D gradient is employed to extract fine details. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences all told. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value. Moreover, our approach can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light. | ['Xiaoning Liu'] | 2022-10-18 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 6.57816052e-01 -9.18897629e-01 4.94384944e-01 -2.31667757e-01
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07af6273-5765-45c1-b382-319acebb5072 | smash-a-semantic-enabled-multi-agent-approach | 2105.14915 | null | https://arxiv.org/abs/2105.14915v1 | https://arxiv.org/pdf/2105.14915v1.pdf | SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT | Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments. | ['Olivier Boissier', 'Fano Ramparany', 'Iago Felipe Trentin', 'Hamed Rahimi'] | 2021-05-31 | null | null | null | null | ['multi-agent-integration'] | ['natural-language-processing'] | [-3.18401694e-01 2.91834831e-01 1.62061676e-01 -4.71544504e-01
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48159a2f-a0e5-452d-aceb-0b3225a40025 | open-world-semi-supervised-generalized | 2305.13533 | null | https://arxiv.org/abs/2305.13533v1 | https://arxiv.org/pdf/2305.13533v1.pdf | Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting | Open-world Relation Extraction (OpenRE) has recently garnered significant attention. However, existing approaches tend to oversimplify the problem by assuming that all unlabeled texts belong to novel classes, thereby limiting the practicality of these methods. We argue that the OpenRE setting should be more aligned with the characteristics of real-world data. Specifically, we propose two key improvements: (a) unlabeled data should encompass known and novel classes, including hard-negative instances; and (b) the set of novel classes should represent long-tail relation types. Furthermore, we observe that popular relations such as titles and locations can often be implicitly inferred through specific patterns, while long-tail relations tend to be explicitly expressed in sentences. Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data. Experimental evaluations on several Open-world RE benchmarks demonstrate that KNoRD consistently outperforms other existing methods, achieving significant performance gains. | ['Jingbo Shang', 'Jiacheng Li', 'William Hogan'] | 2023-05-22 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 8.09026733e-02 5.94330549e-01 -7.79875278e-01 -5.78532040e-01
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edb946b5-617d-412a-972d-86b02160789f | topological-pooling-on-graphs | 2303.14543 | null | https://arxiv.org/abs/2303.14543v1 | https://arxiv.org/pdf/2303.14543v1.pdf | Topological Pooling on Graphs | Graph neural networks (GNNs) have demonstrated a significant success in various graph learning tasks, from graph classification to anomaly detection. There recently has emerged a number of approaches adopting a graph pooling operation within GNNs, with a goal to preserve graph attributive and structural features during the graph representation learning. However, most existing graph pooling operations suffer from the limitations of relying on node-wise neighbor weighting and embedding, which leads to insufficient encoding of rich topological structures and node attributes exhibited by real-world networks. By invoking the machinery of persistent homology and the concept of landmarks, we propose a novel topological pooling layer and witness complex-based topological embedding mechanism that allow us to systematically integrate hidden topological information at both local and global levels. Specifically, we design new learnable local and global topological representations Wit-TopoPool which allow us to simultaneously extract rich discriminative topological information from graphs. Experiments on 11 diverse benchmark datasets against 18 baseline models in conjunction with graph classification tasks indicate that Wit-TopoPool significantly outperforms all competitors across all datasets. | ['Yulia R. Gel', 'Yuzhou Chen'] | 2023-03-25 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-7.93355256e-02 1.84833080e-01 -2.76463628e-01 -2.52940208e-01
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4fc47f66-43ed-4042-8d5a-96abbc38e59a | rationale-augmented-ensembles-in-language | 2207.00747 | null | https://arxiv.org/abs/2207.00747v1 | https://arxiv.org/pdf/2207.00747v1.pdf | Rationale-Augmented Ensembles in Language Models | Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks. We reconsider rationale-augmented prompting for few-shot in-context learning, where (input -> output) prompts are expanded to (input, rationale -> output) prompts. For rationale-augmented prompting we demonstrate how existing approaches, which rely on manual prompt engineering, are subject to sub-optimal rationales that may harm performance. To mitigate this brittleness, we propose a unified framework of rationale-augmented ensembles, where we identify rationale sampling in the output space as the key component to robustly improve performance. This framework is general and can easily be extended to common natural language processing tasks, even those that do not traditionally leverage intermediate steps, such as question answering, word sense disambiguation, and sentiment analysis. We demonstrate that rationale-augmented ensembles achieve more accurate and interpretable results than existing prompting approaches--including standard prompting without rationales and rationale-based chain-of-thought prompting--while simultaneously improving interpretability of model predictions through the associated rationales. | ['Denny Zhou', 'Ed Chi', 'Quoc Le', 'Dale Schuurmans', 'Jason Wei', 'Xuezhi Wang'] | 2022-07-02 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.47509575e-01 2.93726027e-01 6.61183745e-02 -8.48204553e-01
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b96b30b4-b643-4a99-b989-38d3d715c353 | continuous-time-spatiotemporal-calibration-of | 2108.07200 | null | https://arxiv.org/abs/2108.07200v1 | https://arxiv.org/pdf/2108.07200v1.pdf | Continuous-Time Spatiotemporal Calibration of a Rolling Shutter Camera---IMU System | The rolling shutter (RS) mechanism is widely used by consumer-grade cameras, which are essential parts in smartphones and autonomous vehicles. The RS effect leads to image distortion upon relative motion between a camera and the scene. This effect needs to be considered in video stabilization, structure from motion, and vision-aided odometry, for which recent studies have improved earlier global shutter (GS) methods by accounting for the RS effect. However, it is still unclear how the RS affects spatiotemporal calibration of the camera in a sensor assembly, which is crucial to good performance in aforementioned applications. This work takes the camera-IMU system as an example and looks into the RS effect on its spatiotemporal calibration. To this end, we develop a calibration method for a RS-camera-IMU system with continuous-time B-splines by using a calibration target. Unlike in calibrating GS cameras, every observation of a landmark on the target has a unique camera pose fitted by continuous-time B-splines. With simulated data generated from four sets of public calibration data, we show that RS can noticeably affect the extrinsic parameters, causing errors about 1$^\circ$ in orientation and 2 $cm$ in translation with a RS setting as in common smartphone cameras. With real data collected by two industrial camera-IMU systems, we find that considering the RS effect gives more accurate and consistent spatiotemporal calibration. Moreover, our method also accurately calibrates the inter-line delay of the RS. The code for simulation and calibration is publicly available. | ['Yukai Lin', 'Qicheng Yuan', 'Yuan Zhuang', 'Jianzhu Huai'] | 2021-08-16 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 6.87527135e-02 -2.64372736e-01 -1.51123583e-01 -2.02362895e-01
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36df7d9a-9f6f-43b9-ba6e-18d9c6eaf890 | analysing-dense-passage-retrieval-for-multi | 2106.08433 | null | https://arxiv.org/abs/2106.08433v2 | https://arxiv.org/pdf/2106.08433v2.pdf | Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering | In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature. | ['Evangelos Kanoulas', 'Svitlana Vakulenko', 'Nikos Voskarides', 'Georgios Sidiropoulos'] | 2021-06-15 | null | https://aclanthology.org/2021.sustainlp-1.7 | https://aclanthology.org/2021.sustainlp-1.7.pdf | emnlp-sustainlp-2021-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.07358086e-03 -1.62105948e-01 -1.65216208e-01 -3.26837935e-02
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8693c6d3-08e4-48de-819c-750f7c4acd6a | food-recommendations-for-reducing-water | null | null | https://www.mdpi.com/2071-1050/14/7/3833 | https://www.mdpi.com/2071-1050/14/7/3833/pdf | Food Recommendations for Reducing Water Footprint | Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food recommendation scheme, mapping the ingredients to the most resource-friendly dishes on the planet and in particular, selecting recipes that contain ingredients that consume as little water as possible for their production. The system proposed here is able to understand the user’s behavior and to suggest tailor-made recipes with lower water quantity used in production. By continuously using the system, the user can gradually reduce their water footprint and benefit from a healthier diet. The proposed recommendation system was compared with the results of two papers available in the literature that represent the state of the art, obtaining similar results. Therefore, the results of the presented recommendation system can be considered reliable. | ['Andrea Turconi', 'Riccardo La Grassa', 'Nicola Landro', 'Ignazio Gallo'] | 2022-04-24 | null | null | null | sustainability-2022-4 | ['food-recommendation'] | ['miscellaneous'] | [-2.70553619e-01 7.44177168e-03 -7.43906438e-01 -2.26491243e-01
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030e5842-469b-4523-a359-7b6064c0f377 | incremental-learning-techniques-for-semantic | 1907.13372 | null | https://arxiv.org/abs/1907.13372v4 | https://arxiv.org/pdf/1907.13372v4.pdf | Incremental Learning Techniques for Semantic Segmentation | Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image classification and object detection while in this work we formally introduce the incremental learning problem for semantic segmentation in which a pixel-wise labeling is considered. To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones. We propose various approaches working both on the output logits and on intermediate features. In opposition to some recent frameworks, we do not store any image from previously learned classes and only the last model is needed to preserve high accuracy on these classes. The experimental evaluation on the Pascal VOC2012 dataset shows the effectiveness of the proposed approaches. | ['Umberto Michieli', 'Pietro Zanuttigh'] | 2019-07-31 | null | null | null | null | ['overlapped-100-5', 'overlapped-10-1', 'disjoint-15-5', 'disjoint-10-1', 'disjoint-15-1', 'overlapped-15-5', 'overlapped-15-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 7.62708962e-01 3.07150811e-01 -9.18361247e-02 -5.40619731e-01
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a0780901-0ecd-46f8-bdd8-1d8b6a465c93 | graph-neural-network-policies-and-imitation | 2210.05252 | null | https://arxiv.org/abs/2210.05252v1 | https://arxiv.org/pdf/2210.05252v1.pdf | Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues | Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans. In practice, they may have to handle simultaneously several domains and tasks. The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multidomain dialogues. However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce. Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues. The reported experiments underline the benefit of structured policies over standard policies. | ['Lina M. Rojas-Barahona', 'Fabrice Lefèvre', 'Tanguy Urvoy', 'Thibault Cordier'] | 2022-10-11 | null | https://aclanthology.org/2022.sigdial-1.10 | https://aclanthology.org/2022.sigdial-1.10.pdf | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 5.10049164e-02 4.89331573e-01 -9.64492410e-02 -2.13091582e-01
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46694094-25a2-4c86-9971-0300cbf70739 | safe-q-learning-for-continuous-time-linear | 2304.13573 | null | https://arxiv.org/abs/2304.13573v1 | https://arxiv.org/pdf/2304.13573v1.pdf | Safe Q-learning for continuous-time linear systems | Q-learning is a promising method for solving optimal control problems for uncertain systems without the explicit need for system identification. However, approaches for continuous-time Q-learning have limited provable safety guarantees, which restrict their applicability to real-time safety-critical systems. This paper proposes a safe Q-learning algorithm for partially unknown linear time-invariant systems to solve the linear quadratic regulator problem with user-defined state constraints. We frame the safe Q-learning problem as a constrained optimal control problem using reciprocal control barrier functions and show that such an extension provides a safety-assured control policy. To the best of our knowledge, Q-learning for continuous-time systems with state constraints has not yet been reported in the literature. | ['Shubhendu Bhasin', 'Soutrik Bandyopadhyay'] | 2023-04-26 | null | null | null | null | ['q-learning'] | ['methodology'] | [-9.15424302e-02 6.72977269e-01 -7.50997066e-01 2.11485654e-01
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dcaa0b39-25e3-4c90-9488-c5062404532e | switch-to-generalize-domain-switch-learning | null | null | https://openreview.net/forum?id=H-iABMvzIc | https://openreview.net/pdf?id=H-iABMvzIc | Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification | This paper considers few-shot learning under the cross-domain scenario. The cross-domain setting imposes a critical challenge, i.e., using very few (support) samples to generalize the already-learned model to a novel domain. We hold a hypothesis, i.e., if a deep model is capable to fast generalize itself to different domains (using very few samples) during training, it will maintain such domain generalization capacity for testing. It motivates us to propose a novel Domain-Switch Learning (DSL) framework. DSL embeds the cross-domain scenario into the training stage in a ``fast switching'' manner. Specifically, DSL uses a single domain for a training iteration and switches into another domain for the following iteration. During the switching, DSL enforces two constraints: 1) the deep model should not over-fit the domain in the current iteration and 2) the deep model should not forget the already-learned knowledge of other domains. These two constraints jointly promote fast generalization across different domains. Experimental results confirm that the cross-domain generalization capacity can be inherited from the training stage to the testing stage, validating our key hypothesis. Consequentially, DSL significantly improves cross-domain few-shot classification and sets up new state of the art (e.g., 74.28% accuracy on CUB for 5-way 5-shot task). | ['Yi Yang', 'Yifan Sun', 'Zhengdong Hu'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.40531862e-01 -1.77113578e-01 -3.75197381e-01 -5.38732708e-01
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71b54459-670e-40ee-9b86-133463081310 | challenges-facing-the-explainability-of-age | 2303.06640 | null | https://arxiv.org/abs/2303.06640v1 | https://arxiv.org/pdf/2303.06640v1.pdf | Challenges facing the explainability of age prediction models: case study for two modalities | The prediction of age is a challenging task with various practical applications in high-impact fields like the healthcare domain or criminology. Despite the growing number of models and their increasing performance, we still know little about how these models work. Numerous examples of failures of AI systems show that performance alone is insufficient, thus, new methods are needed to explore and explain the reasons for the model's predictions. In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) for age prediction focusing on two specific modalities, EEG signal and lung X-rays. We share predictive models for age to facilitate further research on new techniques to explain models for these modalities. | ['Przemyslaw Biecek', 'Jacek Rogala', 'Jaroslaw Zygierewicz', 'Weronika Hryniewska-Guzik', 'Mikolaj Spytek'] | 2023-03-12 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.96809147e-02 4.67651486e-01 -3.60803008e-01 -6.99934781e-01
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8a2303b8-2ca7-49e2-81c4-be93b77a740c | social-biases-in-automatic-evaluation-metrics | 2210.08859 | null | https://arxiv.org/abs/2210.08859v1 | https://arxiv.org/pdf/2210.08859v1.pdf | Social Biases in Automatic Evaluation Metrics for NLG | Many studies have revealed that word embeddings, language models, and models for specific downstream tasks in NLP are prone to social biases, especially gender bias. Recently these techniques have been gradually applied to automatic evaluation metrics for text generation. In the paper, we propose an evaluation method based on Word Embeddings Association Test (WEAT) and Sentence Embeddings Association Test (SEAT) to quantify social biases in evaluation metrics and discover that social biases are also widely present in some model-based automatic evaluation metrics. Moreover, we construct gender-swapped meta-evaluation datasets to explore the potential impact of gender bias in image caption and text summarization tasks. Results show that given gender-neutral references in the evaluation, model-based evaluation metrics may show a preference for the male hypothesis, and the performance of them, i.e. the correlation between evaluation metrics and human judgments, usually has more significant variation after gender swapping. | ['Xiaojun Wan', 'Mingqi Gao'] | 2022-10-17 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-4.10604514e-02 4.90161151e-01 -3.85838836e-01 -7.14731216e-01
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f677e065-8258-43b4-b339-e691492f1a31 | federated-distillation-of-natural-language | 2110.02432 | null | https://arxiv.org/abs/2110.02432v2 | https://arxiv.org/pdf/2110.02432v2.pdf | KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks | We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network. KNOT aims to train a (global) student model by learning to minimize the optimal transport cost of its assigned probability distribution over the labels to the weighted sum of probabilities predicted by the (local) teacher models, under the constraints, that the student model does not have access to teacher models' parameters or training data. To evaluate the quality of knowledge transfer, we introduce a new metric, Semantic Distance (SD), that measures semantic closeness between the predicted and ground truth label distributions. The proposed method shows improvements in the global model's SD performance over the baseline across three NLP tasks while performing on par with Entropy-based distillation on standard accuracy and F1 metrics. The implementation pertaining to this work is publicly available at: https://github.com/declare-lab/KNOT. | ['Soujanya Poria', 'Tushar Vaidya', 'Rishabh Bhardwaj'] | 2021-10-06 | federated-distillation-of-natural-language-1 | https://aclanthology.org/2022.coling-1.425 | https://aclanthology.org/2022.coling-1.425.pdf | coling-2022-10 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.18743412e-01 8.21800172e-01 -2.70006388e-01 -6.58433378e-01
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a8408a8b-42e5-4802-aaab-d3bdef2dccc8 | pednet-a-persona-enhanced-dual-alternating | null | null | https://aclanthology.org/2020.coling-main.361 | https://aclanthology.org/2020.coling-main.361.pdf | PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation | Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information. However, generating personalized responses is still a challenging task since the leverage of predefined persona information is often insufficient. To alleviate this problem, we propose a novel Persona Enhanced Dual Alternating Learning Network (PEDNet) aiming at producing more personalized responses in various open-domain conversation scenarios. PEDNet consists of a Context-Dominate Network (CDNet) and a Persona-Dominate Network (PDNet), which are built upon a common encoder-decoder backbone. CDNet learns to select a proper persona as well as ensure the contextual relevance of the predicted response, while PDNet learns to enhance the utilization of persona information when generating the response by weakening the disturbance of specific content in the conversation context. CDNet and PDNet are trained alternately using a multi-task training approach to equip PEDNet with the both capabilities they have learned. Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that our method could deliver more personalized responses than baseline methods. | ['Liang Pang', 'Shihan Wang', 'Chao Yang', 'Jingxu Yang', 'Wanyue Zhou', 'Bin Jiang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 1.01908930e-01 4.98564601e-01 3.33448023e-01 -6.55956388e-01
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de0417b6-4080-4928-9100-2d043323c7b0 | audio-visual-segmentation-with-semantics | 2301.13190 | null | https://arxiv.org/abs/2301.13190v1 | https://arxiv.org/pdf/2301.13190v1.pdf | Audio-Visual Segmentation with Semantics | We propose a new problem called audio-visual segmentation (AVS), in which the goal is to output a pixel-level map of the object(s) that produce sound at the time of the image frame. To facilitate this research, we construct the first audio-visual segmentation benchmark, i.e., AVSBench, providing pixel-wise annotations for sounding objects in audible videos. It contains three subsets: AVSBench-object (Single-source subset, Multi-sources subset) and AVSBench-semantic (Semantic-labels subset). Accordingly, three settings are studied: 1) semi-supervised audio-visual segmentation with a single sound source; 2) fully-supervised audio-visual segmentation with multiple sound sources, and 3) fully-supervised audio-visual semantic segmentation. The first two settings need to generate binary masks of sounding objects indicating pixels corresponding to the audio, while the third setting further requires generating semantic maps indicating the object category. To deal with these problems, we propose a new baseline method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process. We also design a regularization loss to encourage audio-visual mapping during training. Quantitative and qualitative experiments on AVSBench compare our approach to several existing methods for related tasks, demonstrating that the proposed method is promising for building a bridge between the audio and pixel-wise visual semantics. Code is available at https://github.com/OpenNLPLab/AVSBench. Online benchmark is available at http://www.avlbench.opennlplab.cn. | ['Yiran Zhong', 'Meng Wang', 'Lingpeng Kong', 'Dan Guo', 'Stan Birchfield', 'Jing Zhang', 'Weixuan Sun', 'Jiayi Zhang', 'Jianyuan Wang', 'Xuyang Shen', 'Jinxing Zhou'] | 2023-01-30 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 3.46257448e-01 -2.94600725e-02 2.57221665e-02 -3.55268776e-01
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5f8f77ba-2306-453c-b8d6-aadfd1328404 | prosody-learning-mechanism-for-speech | 2008.05656 | null | https://arxiv.org/abs/2008.05656v1 | https://arxiv.org/pdf/2008.05656v1.pdf | Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit | Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together. In this paper, a prosody learning mechanism is proposed to model the prosody of speech based on TTS system, where the prosody information of speech is extracted from the melspectrum by a prosody learner and combined with the phoneme sequence to reconstruct the mel-spectrum. Meanwhile, the sematic features of text from the pre-trained language model is introduced to improve the prosody prediction results. In addition, a novel self-attention structure, named as local attention, is proposed to lift this restriction of input text length, where the relative position information of the sequence is modeled by the relative position matrices so that the position encodings is no longer needed. Experiments on English and Mandarin show that speech with more satisfactory prosody has obtained in our model. Especially in Mandarin synthesis, our proposed model outperforms baseline model with a MOS gap of 0.08, and the overall naturalness of the synthesized speech has been significantly improved. | ['Jianzong Wang', 'Zhen Zeng', 'Jing Xiao', 'Ning Cheng'] | 2020-08-13 | null | null | null | null | ['prosody-prediction'] | ['natural-language-processing'] | [-3.65978777e-02 -4.52473313e-02 -5.06475687e-01 -2.58712232e-01
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0e9d14e7-cb40-428f-a6c1-bdf94d1152db | learning-dense-features-for-point-cloud | 2206.06731 | null | https://arxiv.org/abs/2206.06731v2 | https://arxiv.org/pdf/2206.06731v2.pdf | Learning Dense Features for Point Cloud Registration Using a Graph Attention Network | Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning based methods require high computing capacity for processing a large number of raw points at the same time, computational capacity limitation is not an issue thanks to the powerful parallel computing process using GPU. In this paper, we introduce a framework that efficiently and economically extracts dense features using a graph attention network for point cloud matching and registration (DFGAT). The detector of the DFGAT is responsible for finding highly reliable key points in large raw data sets. The descriptor of the DFGAT takes these keypoints combined with their neighbors to extract invariant density features in preparation for the matching. The graph attention network (GAT) uses the attention mechanism that enriches the relationships between point clouds. Finally, we consider this as an optimal transport problem and use the Sinkhorn algorithm to find positive and negative matches. We perform thorough tests on the KITTI dataset and evaluate the effectiveness of this approach. The results show that this method with the efficiently compact keypoint selection and description can achieve the best performance matching metrics and reach the highest success ratio of 99.88% registration in comparison with other state of the art approaches. | ['Quoc Vinh Lai Dang', 'Hojun Jin', 'Sarvar Hussain Nengroo'] | 2022-06-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.19931409e-01 -4.16955650e-01 -4.83466722e-02 -7.51883760e-02
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b604c9c2-7ba1-4766-9b29-b563d6107fbb | multi-genre-music-transformer-composing-full | 2301.02385 | null | https://arxiv.org/abs/2301.02385v1 | https://arxiv.org/pdf/2301.02385v1.pdf | Multi-Genre Music Transformer -- Composing Full Length Musical Piece | In the task of generating music, the art factor plays a big role and is a great challenge for AI. Previous work involving adversarial training to produce new music pieces and modeling the compatibility of variety in music (beats, tempo, musical stems) demonstrated great examples of learning this task. Though this was limited to generating mashups or learning features from tempo and key distributions to produce similar patterns. Compound Word Transformer was able to represent music generation task as a sequence generation challenge involving musical events defined by compound words. These musical events give a more accurate description of notes progression, chord change, harmony and the art factor. The objective of the project is to implement a Multi-Genre Transformer which learns to produce music pieces through more adaptive learning process involving more challenging task where genres or form of the composition is also considered. We built a multi-genre compound word dataset, implemented a linear transformer which was trained on this dataset. We call this Multi-Genre Transformer, which was able to generate full length new musical pieces which is diverse and comparable to original tracks. The model trains 2-5 times faster than other models discussed. | ['Abhinav Kaushal Keshari'] | 2023-01-06 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.03284460e-01 -9.02593285e-02 3.26446593e-01 1.44489259e-01
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bd4a9cb2-21f2-4617-b26e-d48ec7c5dbb9 | gpu-acclerated-automated-feature-extraction | 1304.3992 | null | http://arxiv.org/abs/1304.3992v1 | http://arxiv.org/pdf/1304.3992v1.pdf | GPU Acclerated Automated Feature Extraction from Satellite Images | The availability of large volumes of remote sensing data insists on higher
degree of automation in feature extraction, making it a need of the hour.The
huge quantum of data that needs to be processed entails accelerated processing
to be enabled.GPUs, which were originally designed to provide efficient
visualization, are being massively employed for computation intensive parallel
processing environments. Image processing in general and hence automated
feature extraction, is highly computation intensive, where performance
improvements have a direct impact on societal needs. In this context, an
algorithm has been formulated for automated feature extraction from a
panchromatic or multispectral image based on image processing techniques. Two
Laplacian of Guassian (LoG) masks were applied on the image individually
followed by detection of zero crossing points and extracting the pixels based
on their standard deviation with the surrounding pixels. The two extracted
images with different LoG masks were combined together which resulted in an
image with the extracted features and edges. Finally the user is at liberty to
apply the image smoothing step depending on the noise content in the extracted
image. The image is passed through a hybrid median filter to remove the salt
and pepper noise from the image. This paper discusses the aforesaid algorithm
for automated feature extraction, necessity of deployment of GPUs for the same;
system-level challenges and quantifies the benefits of integrating GPUs in such
environment. The results demonstrate that substantial enhancement in
performance margin can be achieved with the best utilization of GPU resources
and an efficient parallelization strategy. Performance results in comparison
with the conventional computing scenario have provided a speedup of 20x, on
realization of this parallelizing strategy. | ['D. Shanmukha Rao', 'K. Phani Tejaswi', 'A. V. V. Prasad', 'Thara Nair'] | 2013-04-15 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 5.83499730e-01 -5.28601527e-01 6.75035655e-01 -1.52026594e-01
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65ac61a1-200b-4fce-851e-a7b0a96a646f | a-graph-to-sequence-model-for-joint-intent | null | null | https://openreview.net/forum?id=T4q0_LdnUbX | https://openreview.net/pdf?id=T4q0_LdnUbX | A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems | Effectively decoding semantic frames in task-oriented dialogue systems remains a challenge, which typically includes intent detection and slot filling. Although RNN-based neural models show promising results by jointly learning of these two tasks, dominant RNNs are primarily focusing on modeling sequential dependencies. Rich graph structure information hidden in the dialogue context is seldomly explored. In this paper, we propose a novel Graph-to-Sequence model to tackle the spoken language understanding problem by modeling both temporal dependencies and structural information in a conversation. We introduce a new Graph Convolutional LSTM (GC-LSTM) encoder to learn the semantics contained in the dialogue dependency graph by incorporating a powerful graph convolutional operator. Our proposed GC-LSTM can not only capture the spatio-temporal semantic features in a dialogue, but also learn the co-occurrence relationship between intent detection and slot filling. Furthermore, a LSTM decoder is utilized to perform final decoding of both slot filling and intent detection, which mutually improves both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms existing models. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['graph-to-sequence', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.99388951e-01 4.99723852e-01 -1.41249433e-01 -7.01550066e-01
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e2701b0f-8ee9-471c-9157-7371b8770124 | towards-a-unified-model-for-generating | 2301.10799 | null | https://arxiv.org/abs/2301.10799v2 | https://arxiv.org/pdf/2301.10799v2.pdf | Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering | The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on separate models to predict answers and generate explanations, leading to less grounded and frequently inconsistent results. To address this, we propose a multitask learning approach towards a Unified Model for Answer and Explanation generation (UMAE). Our approach involves the addition of artificial prompt tokens to training data and fine-tuning a multimodal encoder-decoder model on a variety of VQA-related tasks. In our experiments, UMAE models surpass the prior state-of-the-art answer accuracy on A-OKVQA by 10~15%, show competitive results on OK-VQA, achieve new state-of-the-art explanation scores on A-OKVQA and VCR, and demonstrate promising out-of-domain performance on VQA-X. | ['Pranava Madhyastha', 'Tillman Weyde', 'Chenxi Whitehouse'] | 2023-01-25 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 7.91641884e-03 4.72078621e-01 -5.82057089e-02 -7.05053568e-01
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85fe8546-cc99-42f8-8308-2ac2cdf7e280 | riddle-lidar-data-compression-with-range-1 | 2206.01738 | null | https://arxiv.org/abs/2206.01738v1 | https://arxiv.org/pdf/2206.01738v1.pdf | RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding | Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data can be represented as two interchangeable representations: 3D point clouds and range images, most previous work focus on compressing the generic 3D point clouds. In this work, we show that directly compressing the range images can leverage the lidar scanning pattern, compared to compressing the unprojected point clouds. We propose a novel data-driven range image compression algorithm, named RIDDLE (Range Image Deep DeLta Encoding). At its core is a deep model that predicts the next pixel value in a raster scanning order, based on contextual laser shots from both the current and past scans (represented as a 4D point cloud of spherical coordinates and time). The deltas between predictions and original values can then be compressed by entropy encoding. Evaluated on the Waymo Open Dataset and KITTI, our method demonstrates significant improvement in the compression rate (under the same distortion) compared to widely used point cloud and range image compression algorithms as well as recent deep methods. | ['Dragomir Anguelov', 'Yin Zhou', 'Charles R. Qi', 'Xuanyu Zhou'] | 2022-06-02 | riddle-lidar-data-compression-with-range | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_RIDDLE_Lidar_Data_Compression_With_Range_Image_Deep_Delta_Encoding_CVPR_2022_paper.pdf | cvpr-2022-1 | ['data-compression'] | ['time-series'] | [ 5.79511523e-01 -2.87164748e-01 -1.70406997e-01 -5.61395049e-01
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d0e582a1-5d4b-4674-99f4-09fffe1abe50 | detecting-adversarial-directions-in-deep | 2306.05873 | null | https://arxiv.org/abs/2306.05873v1 | https://arxiv.org/pdf/2306.05873v1.pdf | Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions | Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in complexity, and furthermore the increase in the dimensions of the observation came at the cost of volatility that can be taken advantage of via adversarial attacks (i.e. moving along worst-case directions in the observation space). To solve this policy instability problem we propose a novel method to detect the presence of these non-robust directions via local quadratic approximation of the deep neural policy loss. Our method provides a theoretical basis for the fundamental cut-off between safe observations and adversarial observations. Furthermore, our technique is computationally efficient, and does not depend on the methods used to produce the worst-case directions. We conduct extensive experiments in the Arcade Learning Environment with several different adversarial attack techniques. Most significantly, we demonstrate the effectiveness of our approach even in the setting where non-robust directions are explicitly optimized to circumvent our proposed method. | ['Jonah Brown-Cohen', 'Ezgi Korkmaz'] | 2023-06-09 | null | null | null | null | ['adversarial-attack', 'atari-games'] | ['adversarial', 'playing-games'] | [ 2.69522481e-02 2.10799471e-01 -2.45961592e-01 1.30129144e-01
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552bb4b2-6357-40c3-b0a0-03aa26c02986 | image-free-domain-generalization-via-clip-for | 2210.16788 | null | https://arxiv.org/abs/2210.16788v1 | https://arxiv.org/pdf/2210.16788v1.pdf | Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation | RGB-based 3D hand pose estimation has been successful for decades thanks to large-scale databases and deep learning. However, the hand pose estimation network does not operate well for hand pose images whose characteristics are far different from the training data. This is caused by various factors such as illuminations, camera angles, diverse backgrounds in the input images, etc. Many existing methods tried to solve it by supplying additional large-scale unconstrained/target domain images to augment data space; however collecting such large-scale images takes a lot of labors. In this paper, we present a simple image-free domain generalization approach for the hand pose estimation framework that uses only source domain data. We try to manipulate the image features of the hand pose estimation network by adding the features from text descriptions using the CLIP (Contrastive Language-Image Pre-training) model. The manipulated image features are then exploited to train the hand pose estimation network via the contrastive learning framework. In experiments with STB and RHD datasets, our algorithm shows improved performance over the state-of-the-art domain generalization approaches. | ['Seungryul Baek', 'Muhammadjon Boboev', 'Jihyeon Kim', 'Dong Uk Kim', 'Hansoo Park', 'Seongyeong Lee'] | 2022-10-30 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-5.77078722e-02 -5.62259376e-01 -2.20223770e-01 -3.88856888e-01
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5da16b77-b8b1-42af-a5e0-c66749486976 | range-only-bearing-estimator-for-localization | 2304.08182 | null | https://arxiv.org/abs/2304.08182v1 | https://arxiv.org/pdf/2304.08182v1.pdf | Range-Only Bearing Estimator for Localization and Mapping | Navigation and exploration within unknown environments are typical examples in which simultaneous localization and mapping (SLAM) algorithms are applied. When mobile agents deploy only range sensors without bearing information, the agents must estimate the bearing using the online distance measurement for the localization and mapping purposes. In this paper, we propose a scalable dynamic bearing estimator to obtain the relative bearing of the static landmarks in the local coordinate frame of a moving agent in real-time. Using contraction theory, we provide convergence analysis of the proposed range-only bearing estimator and present upper and lower-bound for the estimator gain. Numerical simulations demonstrate the effectiveness of the proposed method. | ['Kerstin Bunte', 'Bayu Jayawardhana', 'Matteo Marcantoni'] | 2023-04-17 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-8.51384103e-02 2.98042390e-02 -1.52278170e-01 -2.61289865e-01
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0b866b8c-2003-4bde-90e0-d013d60299c5 | alquist-2-0-alexa-prize-socialbot-based-on | 2011.03259 | null | https://arxiv.org/abs/2011.03259v1 | https://arxiv.org/pdf/2011.03259v1.pdf | Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models | This paper presents the second version of the dialogue system named Alquist competing in Amazon Alexa Prize 2018. We introduce a system leveraging ontology-based topic structure called topic nodes. Each of the nodes consists of several sub-dialogues, and each sub-dialogue has its own LSTM-based model for dialogue management. The sub-dialogues can be triggered according to the topic hierarchy or a user intent which allows the bot to create a unique experience during each session. | ['Jan Šedivý', 'Martin Matulík', 'Jakub Konrád', 'Petr Marek', 'Jan Pichl'] | 2020-11-06 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-4.35394436e-01 8.89925957e-01 -2.51365572e-01 -4.59228605e-01
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f8cf80e3-26eb-425d-8bba-5aa89ca8b583 | covtanet-a-hybrid-tri-level-attention-based | 2101.00691 | null | https://arxiv.org/abs/2101.00691v1 | https://arxiv.org/pdf/2101.00691v1.pdf | CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans | Rapid and precise diagnosis of COVID-19 is one of the major challenges faced by the global community to control the spread of this overgrowing pandemic. In this paper, a hybrid neural network is proposed, named CovTANet, to provide an end-to-end clinical diagnostic tool for early diagnosis, lesion segmentation, and severity prediction of COVID-19 utilizing chest computer tomography (CT) scans. A multi-phase optimization strategy is introduced for solving the challenges of complicated diagnosis at a very early stage of infection, where an efficient lesion segmentation network is optimized initially which is later integrated into a joint optimization framework for the diagnosis and severity prediction tasks providing feature enhancement of the infected regions. Moreover, for overcoming the challenges with diffused, blurred, and varying shaped edges of COVID lesions with novel and diverse characteristics, a novel segmentation network is introduced, namely Tri-level Attention-based Segmentation Network (TA-SegNet). This network has significantly reduced semantic gaps in subsequent encoding decoding stages, with immense parallelization of multi-scale features for faster convergence providing considerable performance improvement over traditional networks. Furthermore, a novel tri-level attention mechanism has been introduced, which is repeatedly utilized over the network, combining channel, spatial, and pixel attention schemes for faster and efficient generalization of contextual information embedded in the feature map through feature re-calibration and enhancement operations. Outstanding performances have been achieved in all three-tasks through extensive experimentation on a large publicly available dataset containing 1110 chest CT-volumes that signifies the effectiveness of the proposed scheme at the current stage of the pandemic. | ['Mohammad Saquib', 'Shaikh Anowarul Fattah', 'Md Maisoon Rahman', 'Shams Nafisa Ali', 'Sakib Chowdhury', 'Md. Jahin Alam', 'Tanvir Mahmud'] | 2021-01-03 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 4.37848806e-01 -2.54646271e-01 -3.46283317e-02 -2.17538372e-01
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25c948d8-9e78-421a-9883-46efcd67e003 | 3dmaterialgan-learning-3d-shape | 2007.13887 | null | https://arxiv.org/abs/2007.13887v1 | https://arxiv.org/pdf/2007.13887v1.pdf | 3DMaterialGAN: Learning 3D Shape Representation from Latent Space for Materials Science Applications | In the field of computer vision, unsupervised learning for 2D object generation has advanced rapidly in the past few years. However, 3D object generation has not garnered the same attention or success as its predecessor. To facilitate novel progress at the intersection of computer vision and materials science, we propose a 3DMaterialGAN network that is capable of recognizing and synthesizing individual grains whose morphology conforms to a given 3D polycrystalline material microstructure. This Generative Adversarial Network (GAN) architecture yields complex 3D objects from probabilistic latent space vectors with no additional information from 2D rendered images. We show that this method performs comparably or better than state-of-the-art on benchmark annotated 3D datasets, while also being able to distinguish and generate objects that are not easily annotated, such as grain morphologies. The value of our algorithm is demonstrated with analysis on experimental real-world data, namely generating 3D grain structures found in a commercially relevant wrought titanium alloy, which were validated through statistical shape comparison. This framework lays the foundation for the recognition and synthesis of polycrystalline material microstructures, which are used in additive manufacturing, aerospace, and structural design applications. | ['B. S. Manjunath', 'Devendra K. Jangid', 'Sam Daly', 'Neal R. Brodnik', 'Amil Khan', 'Tresa M. Pollock', 'McLean P. Echlin'] | 2020-07-27 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 5.71666956e-01 4.10631239e-01 3.10500503e-01 -1.62988678e-01
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d4b78357-995d-4396-a61a-0d990d767a89 | bi-directional-interaction-network-for-person | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Bi-Directional_Interaction_Network_for_Person_Search_CVPR_2020_paper.pdf | Bi-Directional Interaction Network for Person Search | Existing works have designed end-to-end frameworks based on Faster-RCNN for person search. Due to the large receptive fields in deep networks, the feature maps of each proposal, cropped from the stem feature maps, involve redundant context information outside the bounding boxes. However, person search is a fine-grained task which needs accurate appearance information. Such context information can make the model fail to focus on persons, so the learned representations lack the capacity to discriminate various identities. To address this issue, we propose a Siamese network which owns an additional instance-aware branch, named Bi-directional Interaction Network (BINet). During the training phase, in addition to scene images, BINet also takes as inputs person patches which help the model discriminate identities based on human appearance. Moreover, two interaction losses are designed to achieve bi-directional interaction between branches at two levels. The interaction can help the model learn more discriminative features for persons in the scene. At the inference stage, only the major branch is applied, so BINet introduces no additional computation. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our BINet achieves state-of-the-art results among end-to-end methods without loss of efficiency.
| [' Tieniu Tan', ' Chunfeng Song', ' Zhaoxiang Zhang', 'Wenkai Dong'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['person-search'] | ['computer-vision'] | [-1.32684931e-01 -3.71639311e-01 9.14124586e-03 -6.00308239e-01
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a3980b3a-e64d-4fae-bfc9-5209e17e7470 | towards-explainable-artificial-intelligence-1 | 2108.00273 | null | https://arxiv.org/abs/2108.00273v2 | https://arxiv.org/pdf/2108.00273v2.pdf | Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents | Traffic accident anticipation is a vital function of Automated Driving Systems (ADSs) for providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a human-interpretable explanation of their decision-making. Although these models perform well, they remain a black-box to the ADS users, thus difficult to get their trust. To this end, this paper presents a Gated Recurrent Unit (GRU) network that learns spatio-temporal relational features for the early anticipation of traffic accidents from dashcam video data. A post-hoc attention mechanism named Grad-CAM is integrated into the network to generate saliency maps as the visual explanation of the accident anticipation decision. An eye tracker captures human eye fixation points for generating human attention maps. The explainability of network-generated saliency maps is evaluated in comparison to human attention maps. Qualitative and quantitative results on a public crash dataset confirm that the proposed explainable network can anticipate an accident on average 4.57 seconds before it occurs, with 94.02% average precision. In further, various post-hoc attention-based XAI methods are evaluated and compared. It confirms that the Grad-CAM chosen by this study can generate high-quality, human-interpretable saliency maps (with 1.23 Normalized Scanpath Saliency) for explaining the crash anticipation decision. Importantly, results confirm that the proposed AI model, with a human-inspired design, can outperform humans in the accident anticipation. | ['Ruwen Qin', 'Yu Li', 'Muhammad Monjurul Karim'] | 2021-07-31 | null | null | null | null | ['accident-anticipation'] | ['computer-vision'] | [ 1.33777320e-01 5.52900493e-01 -1.44119024e-01 -4.96777475e-01
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6905df0d-448f-4fbe-bc39-be144563126f | cs-trd-a-cross-sections-tree-ring-detection | 2305.10809 | null | https://arxiv.org/abs/2305.10809v1 | https://arxiv.org/pdf/2305.10809v1.pdf | CS-TRD: a Cross Sections Tree Ring Detection method | This work describes a Tree Ring Detection method for complete Cross-Sections of trees (CS-TRD). The method is based on the detection, processing, and connection of edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector ($\sigma$ and two thresholds), a resize factor, the number of rays, and the pith location. The first five parameters are fixed by default. The pith location can be marked manually or using an automatic pith detection algorithm. Besides the pith localization, the CS-TRD method is fully automated and achieves an F-Score of 89\% in the UruDendro dataset (of Pinus Taeda) with a mean execution time of 17 seconds and of 97\% in the Kennel dataset (of Abies Alba) with an average execution time 11 seconds. | ['Gregory Randall', 'Diego Passarella', 'Henry Marichal'] | 2023-05-18 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.59238964e-01 -8.94386917e-02 6.75167367e-02 -1.15803808e-01
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-4.29324001e-01 -7.44075835e-01 -3.34962070e-01 4.44198288e-02] | [8.337270736694336, -1.4620441198349] |
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