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IEEE International Conference on Signal and Image processing (ICSIP)

作者： Gang Xiong Baigang Huang Science and Technology on Communication Information Security Control Lab. Jiaxing China Shanghai Key Lab. for Intelligent Sensing and Recognition SJTU Shanghai China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

In this paper, the radar clutter reconstruction model based on singularity power spectrum (SPS) and instantaneous singularity exponent (ISE) is proposed. The proposed ISE-SPS model consists of power measure channel and dimension measure channel. The multi-scale wavelet coefficients are estimated based on the coupling of the two channels to reconstruct the radar sea clutter. Experiment based on the Ice Multi-parameter Imaging X-Band (IPIX) indicates that the proposed model behaves better than the N-partitioned random multiplicative model (NRMM), with about 32% increment in terms of correlation coefficient and normalized MSE of MFS and SPS.

关键词： Power measurement Radar clutter Sea measurements Radar imaging Clutter Image reconstruction Wavelet coefficients

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Answer-supervised question reformulation for enhancing conversational machine comprehension 2

Answer-supervised question reformulation for enhancing conve...

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2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019

作者： Li, Qian Su, Hui Niu, Cheng Wang, Daling Li, Zekang Feng, Shi Zhang, Yifei School of Computer Science and Engineering Northeastern University Shenyang China Pattern Recognition Center WeChat Ai Tencent Inc China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781950737819

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question lab.ls annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset. © 2019 MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering. All rights reserved.

关键词： Reinforcement learning

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Bridging text and video: A universal multimodal transformer for video-audio scene-aware dialog

arXiv

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arXiv 2020年

作者： Li, Zekang Li, Zongjia Zhang, Jinchao Feng, Yang Niu, Cheng Zhou, Jie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Pattern Recognition Center WeChat AI Tencent Inc China School of EECS Peking University University of Chinese Academy of Sciences

Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System technology Challenge (DSTC8). To solve the task, we propose a universal multimodal transformer and introduce the multitask learning method to learn joint representations among different modalities as well as generate informative and fluent responses. Our method extends the natural language generation pre-trained model to multimodal dialogue generation task. Our system achieves the best performance in both objective and subjective evaluations in the challenge. Copyright © 2020, The Authors. All rights reserved.

关键词： Natural language processing systems

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Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection

arXiv

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arXiv 2021年

作者： Wang, Li Du, Liang Ye, Xiaoqing Fu, Yanwei Guo, Guodong Xue, Xiangyang Feng, Jianfeng Zhang, Li School of Data Science Fudan University. China School of Computer Science Fudan University. China Shanghai Key Lab of Intelligent Information Processing The School of Data Science Fudan University Moe Frontiers Center for Brain Science China The Institute of Science and Technology for Brain-Inspired Intelligence Fudan University. China

The objective of this paper is to learn context- A nd depthaware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context;(ii) this is achieved by first adaptively sampling context-aware nodes in the image context and then dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information;(iii) by augmenting a center-aware depth encoding (CDE) task, our method successfully alleviates the inaccurate depth prior;(iv) we thoroughly demonstrate the effectiveness of our proposed approach and show state-of-the-art results among the monocular-based approaches on the KITTI benchmark dataset. Particularly, we rank 1st in the highly competitive KITTI monocular 3D object detection track on the submission day (November 16th, 2020). © 2021, CC BY.

关键词： Object recognition

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Video Anomaly Detection Using Open Data Filter and Domain Adaptation

Video Anomaly Detection Using Open Data Filter and Domain Ad...

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IEEE Visual Communications and Image processing (VCIP)

作者： Chen Zhang Guorong Li Li Su Weigang Zhang Qingming Huang School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Harbin Institute of Technology Weihai China Key Lab of Intelligent Information Processing ICT CAS Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Video anomaly detection is a very challenging task because of the rarity, openness, and the definition of the anomalies. Researchers pay more attention to the characteristics of anomalies and have proposed a variety of anomaly detection models. However, most existing methods only use normal events to construct anomaly detection models and ignore the diversity and openness of normal events. Actually, because real-world video data often have an open-ended distribution, some normal patterns hardly ever appeared in the training data. In addition, analogous to human experience in identifying anomalies, rare abnormal events can play a certain role in the detection of similar abnormal events in the dataset. Therefore, assuming that a small number of abnormal events are known, we propose a novel supervised anomaly detection model which explicitly detects open normal events and open abnormal events in the dataset and treats open data and seen data with different classifiers. First, we use the training video to train an imbalanced classifier as the seen data classifier. Then, during the testing phase, an open data filter module isused to divide the test data into seen data and open data. Finally, we directly use the seen data classifier to generate anomaly scores for the seen test data. For the open test data, we adopt a domain adaptation method to reduce the distribution difference between it and the training data and train a new classifier to score for it. Extensive experimental results prove the effectiveness of our model.

关键词： Training Training data Anomaly detection Feature extraction Data models Testing Adaptation models

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and lab.r-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

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LogoDet-3K: A large-scale image dataset for logo detection

arXiv

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arXiv 2020年

作者： Wang, Jing Min, Weiqing Hou, Sujuan Ma, Shengnan Zheng, Yuanjie Jiang, Shuqiang School of Information Science and Engineering Shandong Normal University Shandong250358 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Logo detection has been gaining considerable attention because of its wide range of applications in the multimedia field, such as copyright infringement detection, brand visibility monitoring, and product brand management on social media. In this paper, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. We describe the collection and annotation process of our dataset, analyze its scale and diversity in comparison to other datasets for logo detection. We further propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the state-of-the-art YOLOv3 framework for large-scale logo detection. Logo-Yolo can solve the problems of multi-scale objects, logo sample imbalance and inconsistent bounding-box regression. It obtains about 4% improvement on the average performance compared with YOLOv3, and greater improvements compared with reported several deep detection models on LogoDet-3K. The evaluations on other three existing datasets further verify the effectiveness of our method, and demonstrate better generalization ability of LogoDet-3K on logo detection and retrieval tasks. The LogoDet-3K dataset is used to promote large-scale logo-related research and it can be found at https://***/Wangjing1551/LogoDet-3K-Dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Exploring Context and Visual Pattern of Relationship for Scene Graph Generation

Exploring Context and Visual Pattern of Relationship for Sce...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Wenbin Wang Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781728132945

Relationship is the core of scene graph, but its prediction is far from satisfying because of its complex visual diversity. To alleviate this problem, we treat relationship as an abstract object, exploring not only significative visual pattern but contextual information for it, which are two key aspects when considering object recognition. Our observation on current datasets reveals that there exists intimate association among relationships. Therefore, inspired by the successful application of context to object-oriented tasks, we especially construct context for relationships where all of them are gathered so that the recognition could benefit from their association. Moreover, accurate recognition needs discriminative visual pattern for object, and so does relationship. In order to discover effective pattern for relationship, traditional relationship feature extraction methods such as using union region or combination of subject-object feature pairs are replaced with our proposed intersection region which focuses on more essential parts. Therefore, we present our so-called Relationship Context - InterSeCtion Region (CISC) method. Experiments for scene graph generation on Visual Genome dataset and visual relationship prediction on VRD dataset indicate that both the relationship context and intersection region improve performances and realize anticipated functions.

关键词： Visualization Line graph object recognition Intersection Crossing Dataset Recognition (Psychology)

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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Self-supervised Representation Learning from Videos for Facial Action Unit Detection

Self-supervised Representation Learning from Videos for Faci...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yong Li Jiabei Zeng Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781728132945

In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations. Inspired by the fact that facial actions are the movements of facial muscles, we depict the movements as the transformation between two face images in different frames and use it as the self-supervisory signal to learn the representations. However, under the uncontrolled condition, the transformation is caused by both facial actions and head motions. To remove the influence by head motions, we propose a Twin-Cycle Autoencoder (TCAE) that can disentangle the facial action related movements and the head motion related ones. Specifically, TCAE is trained to respectively change the facial actions and head poses of the source face to those of the target face. Our experiments validate TCAE's capability of decoupling the movements. Experimental results also demonstrate that the learned representation is discriminative for AU detection, where TCAE outperforms or is comparable with the state-of-the-art self-supervised learning methods and supervised AU detection methods.

关键词： Face Videotapes Head motions Gold African Union ASTRONOMICAL UNIT Movements Facial Muscles Driving head Action

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