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arXiv 2021年

作者： Su, Binyi Zhou, Zhong Chen, Haiyong Cao, Xiaochun State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Peng Cheng Laboratory Shenzhen518055 China

Solar cell electroluminescence (EL) defect segmentation is an interesting and challenging topic. Many methods have been proposed for EL defect detection, but these methods are still unsatisfactory due to the diversity of the defect and background. In this paper, we provide a new idea of using generative adversarial network (GAN) for defect segmentation. Firstly, the GAN-based method removes the defect region in the input defective image to get a defect-free image, while keeping the background almost unchanged. Then, the subtracted image is obtained by making difference between the defective input image with the generated defect-free image. Finally, the defect region can be segmented through thresholding the subtracted image. To keep the background unchanged before and after image generation, we propose a novel strong identity GAN (SIGAN), which adopts a novel strong identity loss to constraint the background consistency. The SIGAN can be used not only for defect segmentation, but also small-samples defective dataset augmentation. Moreover, we release a new solar cell EL image dataset named as EL-2019, which includes three types of images: crack, finger interruption and defect-free. Experiments on EL-2019 dataset show that the proposed method achieves 90.34% F-score, which outperforms many state-of-the-art methods in terms of solar cell defects segmentation results. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Hybrid features for skeleton-based action recognition based on network fusion

Hybrid features for skeleton-based action recognition based ...

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作者： Chen, Zhangmeng Pan, Junjun Yang, Xiaosong Qin, Hong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Faculty of Media and Communication Bournemouth University Dorset United Kingdom Department of Computer Science Stony Brook University Stony BrookNY United States

In recent years, the topic of skeleton-based human action recognition has attracted significant attention from researchers and practitioners in graphics, vision, animation, and virtual environments. The most fundamental issue is how to learn an effective and accurate representation from spatiotemporal action sequences towards improved performance, and this article aims to address the aforementioned challenge. In particular, we design a novel method of hybrid features' extraction based on the construction of multistream networks and their organic fusion. First, we train a convolution neural networks (CNN) model to learn CNN-based features with the raw skeleton coordinates and their temporal differences serving as input signals. The attention mechanism is injected into the CNN model to weigh more effective and important information. Then, we employ long short-term memory (LSTM) to obtain long-term temporal features from action sequences. Finally, we generate the hybrid features by fusing the CNN and LSTM networks, and we classify action types with the hybrid features. The extensive experiments are performed on several large-scale publically available databases, and promising results demonstrate the efficacy and effectiveness of our proposed framework. © 2020 John Wiley & Sons, Ltd.

关键词： Long short-term memory

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Deep multimodality learning for UAV video aesthetic quality assessment

arXiv

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arXiv 2020年

作者： Kuang, Qi Jin, Xin Zhao, Qinping Zhou, Bin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Electronic Science and Technology Institute Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China

Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and aesthetic-based UAV path planning using the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Antennas

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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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Guiding reinforcement learning with shaping rewards provided by the vision–language model

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Engineering Applications of Artificial Intelligence 2025年 155卷

作者： Wang, Kaiyi Zhao, Yongjia He, Yichen Dai, Shuling Zhang, Ning Yang, Minghao China's State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Jiangxi Research Institute Beihang University Nanchang330096 China Institute of Intelligent Information Processing Beihang University Beijing100191 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

Enabling robots to learn manipulation tasks is a practical engineering application, and reinforcement learning is one of the key artificial intelligence methods to achieve it, but reinforcement learning always faces the trade-off between the ease of designing reward functions and the ease of learning from rewards. Reward shaping provides a solution and recent works have designed rewards using images and language descriptions, which is a simple and convenient shaping method for non-expert users. However, some of them only adopt the pretrained model without fine-tuning, while others train the reward model with absolute score labels, which all have difficulties in capturing the spatial relationships within the images, so the performance of reward shaping models is limited. In this work, we propose a novel reward shaping method to generate additional rewards from task descriptions and scene images. We utilize the pretrained vision–language model as backbone for efficient cross-modal information fusion and design a downstream task trained with pair-wise comparison for reward shaping. Extensive experiments are conducted to demonstrate the effectiveness of each component in the method. The approach is validated in the Meta-World environment and the results demonstrate that it outperforms standard reinforcement learning and the existing work in terms of the policy learning efficiency. © 2025 Elsevier Ltd

关键词： Reinforcement learning

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Shape-Aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains 23rd

Shape-Aware Meta-learning for Generalizing Prostate MRI Segm...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Liu, Quande Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong T Stone Robotics Institute The Chinese University of Hong Kong Shatin Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597122

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains (Code and dataset are available at https://***/liuquande/SAML). © 2020, Springer Nature Switzerland AG.

关键词： Learning systems

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The Cutting Force Simulation of 30CrMnSiA Based on AdvantEdge 2D Broaching

The Cutting Force Simulation of 30CrMnSiA Based on AdvantEdg...

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作者： Zheng Hu Wanhao Zhang Xin Li Hanjun Gao Science and Technology on Vehicle Transmission Laboratory China North Vehicle Research Institute State Key Laboratory of Virtual Reality Technology and Systems School of Mechanical Engineering and Automation Beihang University

30CrMnSiA alloy steel is a kind of medium carbon quenched and tempered structural steel commonly used in machinery manufacturing industry, which has superior comprehensive mechanical and technological properties. In view of the great difficulty in solving the cutting process of metal by analytical method, this paper simulates the broaching process of 30CrMnSiA by using AdvantEdge software, and studies the influence of cutting parameters, tool geometry parameters, heat exchange coefficient of coolant and other factors on the cutting force. The results show that the cutting force increases with the increase of cutting width, cutting thickness, friction coefficient between tool and chip, and cutting edge radius, while decreases with the increase of relief angle. The cutting force decreases slightly with increasing of rake angle, and it almost does not change with the increase of cutting speed,coating thickness and the heat exchange coefficient of coolant.

关键词： The Cutting Force Simulation of 30CrMnSiA Based on AdvantEdge 2D Broaching AdvantEdge

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Distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shucheng Hu Tao Ren Jianwei Niu Zheyuan Hu Guoliang Xing State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China The Chinese University of Hong Kong HKSAR China

ISBN: (纸本)9781665406697

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the ‘curse of dimensionality’ in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： Training Solid modeling Processor scheduling Simulation Reinforcement learning virtual reality Scheduling

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Multi-Agent Cluster systems Formation Control with Obstacle Avoidance

Multi-Agent Cluster Systems Formation Control with Obstacle ...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yi Sun Xiaoguang Hu Jin Xiao Guofeng Zhang Shaojie Wang Lei Liu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Electro Mechanical Engineering Institute Beijing P R China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For formation control and obstacle avoidance of multi-agent cluster systems, this paper proposes a new formation control and obstacle avoidance algorithm based on artificial potential field method, the advantage of which is that it can enhance the robustness of multi-agent systems and make the overall performance as no central control. Formation control adopts the follower-leader method. Leaders are not artificially designated, but selected by autonomous decision-making of multi-agent systems. When the selected leader fails to continue to perform the task, the remaining followers can independently choose a new leader and form a new formation to continue the task according to the number of existing agents. Finally, the effectiveness of the proposed method is verified by a numerical simulation. The simulation results show that the algorithm proposed in this paper can perform no central formation control and obstacle avoidance for multi-agent systems.

关键词： Simulation Clustering algorithms Control systems Robustness Collision avoidance Task analysis Multi-agent systems

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Entity concept-enhanced few-shot relation extraction

arXiv

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arXiv 2021年

作者： Yang, Shan Zhang, Yongfei Niu, Guanglin Zhao, Qinhua Pu, Shiliang Beijing Key Laboratory of Digital Media School of Computer Science and Engineering BeiHang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing100191 China Pengcheng Laboratory Shenzhen518055 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering BeiHang University Beijing100191 China Hikvision Research Institute Hangzhou311500 China

Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge. To address this problem, in this paper, we proposed a novel entity CONCEPTenhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance. Firstly, a conceptsentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts. Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces. Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines. Code is available at https://***/LittleGuoKe/ConceptFERE. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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