Structured Context Learning for Generic Event Boundary Detection

• URL: http://arxiv.org/abs/2512.00475v1
• Date: Sat, 29 Nov 2025 13:06:52 GMT
• Title: Structured Context Learning for Generic Event Boundary Detection
• Authors: Xin Gu, Congcong Li, Xinyao Wang, Dexiang Hong, Libo Zhang, Tiejian Luo, Longyin Wen, Heng Fan,
• Abstract summary: Generic Event Boundary Detection aims to identify moments in videos that humans perceive as event boundaries.<n>This paper proposes a novel method for addressing this task, called Structured Context Learning.<n>Our approach is end-to-end trainable and flexible, not restricted to specific temporal models like GRU, LSTM, and Transformers.
• Score: 34.30144454487081
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, called Structured Context Learning, which introduces the Structured Partition of Sequence (SPoS) to provide a structured context for learning temporal information. Our approach is end-to-end trainable and flexible, not restricted to specific temporal models like GRU, LSTM, and Transformers. This flexibility enables our method to achieve a better speed-accuracy trade-off. Specifically, we apply SPoS to partition the input frame sequence and provide a structured context for the subsequent temporal model. Notably, SPoS's overall computational complexity is linear with respect to the video length. We next calculate group similarities to capture differences between frames, and a lightweight fully convolutional network is utilized to determine the event boundaries based on the grouped similarity maps. To remedy the ambiguities of boundary annotations, we adapt the Gaussian kernel to preprocess the ground-truth event boundaries. Our proposed method has been extensively evaluated on the challenging Kinetics-GEBD, TAPOS, and shot transition detection datasets, demonstrating its superiority over existing state-of-the-art methods.

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