学习检测、关联和识别未修剪视频中的人类行为和周围场景

Jungin Park, Sangryul Jeon, Seungryong Kim, Jiyoung Lee, Sunok Kim, K. Sohn
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引用次数: 3

摘要

虽然识别人类行为和周围场景解决了视频理解的不同方面,但它们具有很强的相关性,可以用来补充彼此的单一信息。在本文中,我们提出了一种基于时间注意技术及其融合的端到端学习框架中制定的联合动作和场景识别方法。将时间关注模块应用到通用特征网络中,有效地提取动作和场景特征,然后通过该融合模块将动作和场景特征合成为单个特征向量。我们在CoVieW18数据集上的实验表明,我们的模型能够在弱监督的情况下检测时间注意,并显著提高了多任务动作和场景分类的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning to Detect, Associate, and Recognize Human Actions and Surrounding Scenes in Untrimmed Videos
While recognizing human actions and surrounding scenes addresses different aspects of video understanding, they have strong correlations that can be used to complement the singular information of each other. In this paper, we propose an approach for joint action and scene recognition that is formulated in an end-to-end learning framework based on temporal attention techniques and the fusion of them. By applying temporal attention modules to the generic feature network, action and scene features are extracted efficiently, and then they are composed to a single feature vector through the proposed fusion module. Our experiments on the CoVieW18 dataset show that our model is able to detect temporal attention with only weak supervision, and remarkably improves multi-task action and scene classification accuracies.
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