Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach

• URL: http://arxiv.org/abs/2206.15268v1
• Date: Thu, 30 Jun 2022 13:19:53 GMT
• Title: Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
• Authors: Jiaqi Tang, Zhaoyang Liu, Jing Tan, Chen Qian, Wayne Wu, Limin Wang
• Abstract summary: Generic event boundary detection (GEBD) is an important yet challenging task in video understanding. We present a local context modeling and global boundary decoding approach for GEBD task.
• Score: 46.97359231258202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. In this paper, we present a local context modeling and global boundary decoding approach for GEBD task. Local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence. Based on them, global boundary decoding sub-network is exploited to decode event boundaries from a global view. Our proposed method achieves 85.13% F1-score on Kinetics-GEBD testing set, which achieves a more than 22% F1-score boost compared to the baseline method. The code is available at https://github.com/JackyTown/GEBD_Challenge_CVPR2022.

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