Global2Local: A Joint-Hierarchical Attention for Video Captioning

• URL: http://arxiv.org/abs/2203.06663v1
• Date: Sun, 13 Mar 2022 14:31:54 GMT
• Title: Global2Local: A Joint-Hierarchical Attention for Video Captioning
• Authors: Chengpeng Dai, Fuhai Chen, Xiaoshuai Sun, Rongrong Ji, Qixiang Ye, Yongjian Wu
• Abstract summary: We propose a novel joint-hierarchical attention model for video captioning, which embeds the key clips, the key frames and the key regions jointly into the captioning model. Such a joint-hierarchical attention model first conducts a global selection to identify key frames, followed by a Gumbel sampling operation to identify further key regions based on the key frames.
• Score: 123.12188554567079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, automatic video captioning has attracted increasing attention, where the core challenge lies in capturing the key semantic items, like objects and actions as well as their spatial-temporal correlations from the redundant frames and semantic content. To this end, existing works select either the key video clips in a global level~(across multi frames), or key regions within each frame, which, however, neglect the hierarchical order, i.e., key frames first and key regions latter. In this paper, we propose a novel joint-hierarchical attention model for video captioning, which embeds the key clips, the key frames and the key regions jointly into the captioning model in a hierarchical manner. Such a joint-hierarchical attention model first conducts a global selection to identify key frames, followed by a Gumbel sampling operation to identify further key regions based on the key frames, achieving an accurate global-to-local feature representation to guide the captioning. Extensive quantitative evaluations on two public benchmark datasets MSVD and MSR-VTT demonstrates the superiority of the proposed method over the state-of-the-art methods.

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