《图形模型》是国际公认的高评价的顶级期刊,专注于图形模型的创建、几何处理、动画和可视化,以及它们在工程、科学、文化和娱乐方面的应用。GMOD为其读者提供了经过彻底审查和精心挑选的论文,这些论文传播令人兴奋的创新,传授严谨的理论基础,提出健壮和有效的解决方案,或描述各种主题中的雄心勃勃的系统或应用程序。 官网地址:http://dblp.uni-trier.de/db/journals/cvgip/

最新论文

Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and reformulate the task of MLIC as an instance-label matching selection problem. To model such problem, we propose a novel deep learning framework named Graph Matching based Multi-Label Image Classification (GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its excellent capability of excavating the instance and label relationship. Specifically, we first construct an instance spatial graph and a label semantic graph respectively, and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Extensive experiments conducted on various image datasets demonstrate the superiority of our proposed method.

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