ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization
arXiv:2605.30689v1 Announce Type: cross Abstract: Zero-shot Temporal Action Localization (ZS-TAL) aims to detect and locate previously unseen actions in untrimmed videos. However, existing approaches primarily focus on modeling long-range contextual information, often neglecting the critical relative-offset-based local correlations between video frames. Furthermore, their performance is hindered by limited feature representation capabilities due to the shallow nature of their network architectures. In this paper, we address these limitations by introducing a novel local-global multi-scale feat
The continuous advancements in AI research, particularly in computer vision and natural language processing, are enabling more sophisticated approaches to video understanding.
Improved zero-shot temporal action localization enhances the ability of AI systems to understand and interpret complex events in untrimmed video, critical for various real-world applications.
This research introduces a method for AI to detect and locate previously unseen actions in videos more effectively by combining local and global temporal information with text enhancements.
- · AI research institutions
- · Video analytics companies
- · Surveillance technology providers
- · Autonomous systems developers
- · Legacy video analysis methods
- · Companies reliant on human video annotation for novel actions
More accurate and efficient automated video content analysis.
Accelerated development of AI applications requiring real-time understanding of novel events, such as in robotics or safety monitoring.
Enhanced automation of tasks that currently require extensive human oversight for identifying new or unexpected activities in video feeds.
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Read at arXiv cs.AI