PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization

arXiv:2403.18915v1 Announce Type: cross
Abstract: This paper introduces a novel approach to temporal action localization (TAL) in few-shot learning. Our work addresses the inherent limitations of conventional single-prompt learning methods that often lead to overfitting due to the inability to generalize across varying contexts in real-world videos. Recognizing the diversity of camera views, backgrounds, and objects in videos, we propose a multi-prompt learning framework enhanced with optimal transport. This design allows the model to learn a set of diverse prompts …

abstract arxiv cs.cv cs.lg few-shot few-shot learning limitations localization novel overfitting paper plot prompt prompt learning temporal transport type videos work world