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Few-shot node classification via local adaptive discriminant structure learning

Frontiers of computer Science 2023年第2期17卷 135-143页

作者： Zhe XUE Junping DU Xin XU Xiangbin LIU Junfu WANG Feifei KOU Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China

Node classification has a wide range of application scenarios such as citation analysis and social network *** many real-world attributed networks,a large portion of classes only contain limited labeled *** of the existing node classification methods cannot be used for few-shot node *** train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node *** aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and *** conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

关键词： few-shot learning node classification graph neural network adaptive structure learning attention strategy

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DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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Computational Visual Media 2023年第4期9卷 859-873页

作者： Zhuangzhuang Miao Yong Zhang Yuan Peng Haocheng Peng Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Taiji Computer Corporation Ltd. China

Crowd counting provides an important foundation for public security and urban *** to the existence of small targets and large density variations in crowd images,crowd counting is a challenging *** methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and ***-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually *** propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd *** main components include a recursive swin transformer and a multi-level dilated convolution regression *** recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global *** multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction *** module can capture both low-and high-level features simultaneously to enhance the receptive *** addition,two regression head fusion mechanisms realize dynamic and mean fusion *** on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

关键词： crowd counting transformer dilated convolution global perspective field pyramid

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Multimodal Interactive Network for Sequential Recommendation

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Journal of computer Science & technology 2023年第4期38卷 911-926页

作者：韩滕跃王鹏飞牛少彰 Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and *** work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse *** data sparse problem now is attracting a lot of attention in both industry and academic ***,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal *** this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient ***,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative ***,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each *** seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential *** is the first time that interactions in each modality have been explicitly discussed and utilized in sequential *** results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

关键词： multi-modality interactive network sequential recommendation

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A method of embedding a high-resolution image into a large field-of-view image

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multimedia Tools and Applications 2025年第14期84卷 13037-13056页

作者： Dong, Yanmei Pei, Mingtao Jia, Yunde Key Laboratory of Intelligent Information Processing and Graph Processing Tus College of Digit Guangxi University of Science and Technology Liuzhou545006 China Beijing Laboratory of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing100081 China

Images captured by wide-angle cameras or fisheye cameras are with large Field-of-View (FOV) but low resolution, while images captured by conventional cameras are with high-resolution but limited FOV. To handle this contradiction, in this paper, we propose to improve the resolution of a large FOV image by embedding a high-resolution image into it. We present an image embedding method based on non-rigid transformations and mesh optimization. Without any radial distortion correction on large FOV images, we utilize non-rigid transformations to model the mathematical transformation relationship between the high-resolution image and the large FOV image. Meanwhile, we exploit mesh optimization to generate evenly distributed control points for maintaining the shape and content of the large FOV image. To verify the effectiveness of the proposed method, we capture some image pairs from a conventional camera and a large FOV camera, and exploit several classic image alignment methods to implement four baseline methods. The experimental results show that our method preserves the shape and content of the original FOV image from 58% to 82%, which significantly outperforms all baseline methods. The results indicate that the proposed method can effectively embed a high-resolution image into a large FOV image as well as preserve the shape and content of the large FOV image. In other words, the resolution of a large FOV image can be improved via high-resolution image embedding. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Multiagent trajectory prediction with global-local scene-enhanced social interaction graph network

Multiagent trajectory prediction with global-local scene-enh...

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作者： Lin, Xuanqi Zhang, Yong Wang, Shun Piao, Xinglin Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing China

Trajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning-based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real-world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global-local scene-enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global-local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios. © 2024 John Wiley & Sons Ltd.

关键词： Multi agent systems

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Robust Multi-Graph Contrastive Network for Incomplete Multi-View Clustering

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IEEE Transactions on multimedia 2025年 27卷 2747-2759页

作者： Xue, Zhe Li, Yawen Guan, Zhongchao Li, Wenling Liang, Meiyu Zhou, Hai Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing100876 China Beijing University of Posts and Telecommunications School of Economics and Management Beijing100876 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

Food categorization is pivotal in numerous aspects of everyday life, assisting in the selection of food, managing diets, and addressing essential survival requirements. By leveraging the complementary information of various views, multi-view learning usually achieves superior performance compared to the single-view learning methods. However, characterized by the unrestrained openness of internet platforms and potential inconsistencies in food data collection processes, multi-view features often suffer from data loss, resulting in incomplete multi-view food data. Conventional multi-view clustering methods often falter in effectively capitalizing on the diverse correlations contained in food data, and exhibit limitations in dealing with the noise and irregularities pervading different views. Addressing these challenges, this paper presents the Robust Multi-Graph Contrastive network (RMGC) for multi-view food clustering. RMGC artfully combines multi-view representation learning with multi-graph contrastive regularization, creating a cohesive framework to manage incomplete multi-view data. By developing a multi-view encoding network, RMGC seamlessly blends various views into a cohesive representation, astutely assessing the significance of each view. More importantly, the proposed robust multi-graph contrastive regularization enhances the precision of the learned representation and successfully counteracts the noise and unreliability in multi-view data. The experiments conducted across several multi-view datasets manifest the effectiveness of RMGC, showing its superiority over existing methods. Our method not only making an advancement in food categorization but also contributes to the broader field of multi-view learning, offering innovative solutions for handling incomplete and noisy multi-view data. © 1999-2012 IEEE.

关键词： Cluster analysis

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Video Super-Resolution Reconstruction Based on Deep Learning and Spatio-Temporal Feature Self-similarity (Extended abstract) 39

Video Super-Resolution Reconstruction Based on Deep Learning...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Liang, Meiyu Du, Junping Li, Linghui Xue, Zhe Wang, Xiaoxiao Kou, Feifei Wang, Xu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing China

ISBN: (纸本)9798350322279

Video super-resolution (SR) reconstruction technology aims at obtaining high quality reconstruction of high-resolution (HR) video sequences by inferring the lost detailed information from their low-resolution (LR) counterparts. However, this technology is an ill-posed problem because significant detailed information is lost in the process of video degrading. The existing learning-based SR reconstruction methods can be adapted to a larger super-resolution factor, but it cannot be guaranteed that any low-resolution image block can find its corresponding high-resolution block matching in a limited-scale training set. Some noise and over smooth phenomenon usually exist while dealing with some unique features that rarely appear in a given training data set. The self-similarity based SR methods do not rely on accurate sub-pixel motion estimation and thus can be adapted to complex motion patterns. However, under conditions of insufficient internal similar blocks, some visual flaws are usually produced due to the mismatched internal instances. © 2023 IEEE.

关键词： Motion estimation

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Scale-Adaptive Multi-area Representation for Instance Segmentation 12th

Scale-Adaptive Multi-area Representation for Instance Segme...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Zhang, Huiyong Wang, Lichun Li, Shuang Xu, Kai Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783031463136

For the instance segmentation task, instance representation directly determines the quality of generated masks, so achieving efficient and accurate instance representation is crucial. Grid-based or box-based instance representation contains redundant information from background or other instances, activation-based instance representation includes a small part of the instance. The instance representation based on current methods is not accurate enough. In order to represent more information of an instance under the condition of excluding irrelevant information, this paper proposes multi-area representation (MAR), which is in the form of a scale-adaptive multi-area activation map generated by a multi-branch structure. MAR can adapt to the structure and pose of an instance, thereby representing the shape and size of the instance. Experiments show that, compared with SparseInst, MARInst can improve the performance of instance segmentation and keep the inference speed and training memory almost unchanged. In particular, MARInst achieved 30.3% AP on the MS COCO 2017 val, and 1.6% AP higher than SparseInst when using the same ResNet-50 backbone, proving the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical activation

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Bug localization based on syntactical and semantic information of source code

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Journal of Systems Engineering and Electronics 2023年第1期34卷 236-246页

作者： YAN Xuefeng CHENG Shasha GUO Liqin College of Computer Science Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement ***, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.

关键词： bug report abstract syntax tree code representation software bug localization

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Deep Learning Based Take-Over Performance Prediction and Its Application on intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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