Structured Context Learning for Generic Event Boundary Detection

2025-11-29

AI Summary

General Event Boundary Detection (GEBD) aims to localize human-perceived natural event transitions in videos. This paper proposes Structured Partitioning-guided Temporal Learning (SPoS), the first end-to-end GEBD framework that does not rely on dedicated sequential models such as Transformers or LSTMs. Its core contributions are: (1) Sequence-structured partitioning (SPoS) to explicitly model temporal context; (2) Group-wise similarity modeling to enhance boundary discriminability; and (3) Gaussian kernel-based label smoothing to improve localization accuracy. Built upon a lightweight fully convolutional architecture, SPoS achieves linear time complexity. Extensive experiments demonstrate state-of-the-art performance on Kinetics-GEBD, TAPOS, and shot transition detection benchmarks—achieving superior accuracy while maintaining low computational overhead.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Structured Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Detects generic event boundaries in videos
• Introduces Structured Partition of Sequence for context
• Achieves linear complexity and speed-accuracy trade-off

Innovation

• Introduces Structured Partition of Sequence for temporal context
• Uses lightweight fully convolutional network for boundary detection
• Applies Gaussian kernel to preprocess ambiguous boundary annotations