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Soft video parsing by label distribution learning

Frontiers of Computer Science 2019年第2期13卷 302-317页

作者： miaogen ling Xin GENG Department of Computer Science and Engineering Southeast University Nanjing 211189 China

In this paper, we tackle the problem of segmenting out a sequence of actions from videos. The videos contain background and actions which are usually composed of ordered sub-actions. We refer the sub-actions and the background as semantic units. Considering the possible overlap between two adjacent semantic units, we propose a bidirectional sliding window method to generate the label distributions for various segments in the video. The label distribution covers a certain number of semantic unit labels, representing the degree to which each label describes the video segment. The mapping from a video segment to its label distribution is then learned by a Label Distribution Learning (LDL) algorithm. Based on the LDL model, a soft video parsing method with segmental regular grammars is proposed to construct a tree structure for the video. Each leaf of the tree stands for a video clip of background or sub-action. The proposed method shows promising results on the THUMOST4, MSR-II and UCF101 datasets and its computational complexity is much less than the compared state-of-the-art video parsing method.

关键词： video parsing label distribution learning subactions graduality

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Label distribution for multimodal machine learning

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Frontiers of Computer Science 2022年第1期16卷 33-43页

作者： Yi REN Ning XU miaogen ling Xin GENG Department of Computer Science and Engineering Southeast UniversityNanjing211189China

Multimodal machine learning(MML)aims to understand the world from multiple related *** has attracted much attention as multimodal data has become increasingly available in real-world *** is shown that MML can perform better than single-modal machine learning,since multi-modalities containing more information which could complement each ***,it is a key challenge to fuse the multi-modalities in *** from previous work,we further consider the side-information,which reflects the situation and influences the fusion of *** recover multimodal label distribution(MLD)by leveraging the side-information,representing the degree to which each modality contributes to describing the ***,a novel framework named multimodal label distribution learning(MLDL)is proposed to recover the MLD,and fuse the multimodalities with its guidance to learn an in-depth understanding of the jointly feature ***,two versions of MLDL are proposed to deal with the sequential *** on multimodal sentiment analysis and disease prediction show that the proposed approaches perform favorably against state-of-the-art methods.

关键词： multimodal machine learning label distribution learning sentiment analysis disease prediction

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