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37th International Conference on Machine Learning, ICML 2020

作者： Yang, Yaodong Hao, Jianye Chen, Guangyong Tang, Hongyao Chen, Yingfeng Hu, Yujing Fan, Changjie Wei, Zhongyu College of Intelligence and Computing Tianjin University China Huawei Noah‘s Ark Lab Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Tencent Quantum Lab NetEase Fuxi AI Lab Fudan University China

ISBN: (纸本)9781713821120

Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A particularly interesting and widely applicable class of problems is the partially observable cooperative multiagent setting, in which a team of agents learns to coordinate their behaviors conditioning on their private observations and commonly shared global reward signals. One natural solution is to resort to the centralized training and decentralized execution paradigm and during centralized training, one key challenge is the multiagent credit assignment: how to allocate the global rewards for individual agent policies for better coordination towards maximizing system-level’s benefits. In this paper, we propose a new method called Q-value Path Decomposition (QPD) to decompose the system’s global Q-values into individual agents’ Q-values. Unlike previous works which restrict the representation relation of the individual Q-values and the global one, we leverage the integrated gradient attribution technique into deep MARL to directly decompose global Q-values along trajectory paths to assign credits for agents. We evaluate QPD on the challenging StarCraft II micromanagement tasks and show that QPD achieves the state-of-the-art performance in both homogeneous and heterogeneous multiagent scenarios compared with existing cooperative MARL algorithms. Copyright 2020 by the author(s).

关键词： Multi agent systems

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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Learning an Effective Charging Scheme for Mobile Devices

Learning an Effective Charging Scheme for Mobile Devices

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tang Liu Baijun Wu Wenzheng Xu Xianbo Cao Jian Peng Hongyi Wu Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China University of Louisiana at Lafayette Lafayette LA USA College of Computer Science Sichuan University Chengdu China Center for Cybersecurity Education and Research Dominion University Norfolk VA USA

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks (WRNs). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. Then, we formalize the effective charging problem as a charging reward maximization problem (CRMP), where the amount of reward obtained by charging a de-vice is inversely proportional to the residual lifetime of the device. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning (RL) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

关键词： Mobile handsets Heuristic algorithms Batteries Trajectory Wireless communication Energy consumption Wireless sensor networks

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Cross-reference stitching quality assessment for 360◦ omnidirectional images 27

Cross-reference stitching quality assessment for 360◦ omnid...

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27th ACM International Conference on Multimedia, MM 2019

作者： Li, Jia Yu, Kaiwen Zhao, Yifan Zhang, Yu Xu, Long State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University China National Astronomical Observatories Chinese Academy of Sciences China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450368896

Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with an immersive experience. However, despite various existing approaches for omnidirectional image stitching, how to quantitatively assess the quality of stitched images is still insufficiently explored. To address this problem, we first establish a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦, 180◦, and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions. Based on this dataset, we propose a set of Omnidirectional Stitching Image Quality Assessment (OS-IQA) metrics. In these metrics, the stitching regions are assessed by exploring the local relationships between the stitched image and its cross-reference with histogram statistics, perceptual hash and sparse reconstruction, while the whole stitched images are assessed by the global indicators of color difference and fitness of blind *** and quantitative experiments show our method outperforms the classic IQA metrics and is highly consistent with human subjective evaluations. To the best of our knowledge, it is the first attempt that assesses the stitching quality of omnidirectional images by using cross-references. © 2019 Association for computing Machinery.

关键词： virtual reality

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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks

arXiv

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arXiv 2020年

作者： Liu, Yunfei Ma, Xingjun Bailey, James Lu, Feng State Key Laboratory of Virtual Reality Technology and Systems School of CSE Beihang University Beijing China Peng Cheng Laboratory Shenzhen China School of Information Technology Deakin University Geelong Australia School of Computing and Information Systems University of Melbourne Australia

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing backdoor attacks are effective, they are not stealthy. The modifications made on training data or labels are often suspicious and can be easily detected by simple data filtering or human inspection. In this paper, we present a new type of backdoor attack inspired by an important natural phenomenon: reflection. Using mathematical modeling of physical reflection models, we propose reflection backdoor (Refool) to plant reflections as backdoor into a victim model. We demonstrate on 3 computer vision tasks and 5 datasets that, Refool can attack state-of-the-art DNNs with high success rate, and is resistant to state-of-the-art backdoor defenses. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Automatic image annotation with real-world community contributed data set

Automatic image annotation with real-world community contrib...

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作者： Tian, Feng Shen, Xukun Shang, Fuhua School of Computer and Information Technology Northeast Petroleum University DaQing163318 China School of Computing National University of Singapore Singapore119077 Singapore State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing100191 China

With the massive explosion of social multimedia community, social images have become very popular in our daily life. The image-associated labels are a valuable resource for automatic image annotation, but they tend to be unreliable. In this paper, we exploit the problem of image annotation from real-world community contributed images and their associated incorrect, insufficient, and personalized labels. We present SNTag, a novel semantic neighborhood learning method, on which image annotation task can be efficiently carried out in real-world scenario. First, we propose to use image-associated labels as the supervising information to guide the replenishment of training images, which enable the labels for training image not only more sufficient, but also more correct. Then, the "semantic balanced neighborhood" for image is generated, thus enabling the presence of more rare labels in image label list. Furthermore, we generate "semantic consistent neighborhood" within corresponding "semantic balanced neighborhood". The retrieved neighbor images are not only visually alike but also semantically related. Contrary to earlier work, these neighbors are retrieved from the same subspace by the integration of metric learning embedded in multiple labels and sparse reconstruction. Based on the neighbor set, we propose a novel algorithm to assign the optimal labels to the image, which is more robust to noise. We conduct extensive experiments on several standard real-world benchmark data sets downloaded from community websites. The experimental results demonstrate that it outperforms the current state-of-the-art methods. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Image annotation

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detecti...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhihao Chen Lei Zhu Liang Wan Song Wang Wei Feng Pheng-Ann Heng College of Intelligence and Computing Tianjin University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Engineering University of South Carolina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.

关键词： Task analysis Image edge detection Feature extraction Training Radio frequency Fuses Solid modeling

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Cooperative bi-path metric for few-shot learning

arXiv

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arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion

arXiv

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arXiv 2020年

作者： Chen, Cheng Dou, Qi Jin, Yueming Chen, Hao Qin, Jing Heng, Pheng-Ann Dept. of Computer Science and Engineering Chinese University of Hong Kong Department of Computing Imperial College Longdon Imsight Medical Technology Co. Ltd. China Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Chinese Academy of Sciences SIAT China

Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter the problem of missing imaging modalities. We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code, which uniquely sticks to each modality, and the modality-invariant content code, which absorbs multimodal information for the segmentation task. With enhanced modality-invariance, the disentangled content code from each modality is fused into a shared representation which gains robustness to missing data. The fusion is achieved via a learning-based strategy to gate the contribution of different modalities at different locations. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset. With competitive performance to the state-of-the-art approaches for full modality, our method achieves outstanding robustness under various missing modality(ies) situations, significantly exceeding the state-of-the-art method by over 16% in average for Dice on whole tumor segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Tumors

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