PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal
  Action Localization
Edward Fish Jon Weinbren Andrew Gilbert
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 for each action,
capturing general characteristics more effectively and distributing the
representation to mitigate the risk of overfitting. Furthermore, by employing
optimal transport theory, we efficiently align these prompts with action
features, optimizing for a comprehensive representation that adapts to the
multifaceted nature of video data. Our experiments demonstrate significant
improvements in action localization accuracy and robustness in few-shot
settings on the standard challenging datasets of THUMOS-14 and EpicKitchens100,
highlighting the efficacy of our multi-prompt optimal transport approach in
overcoming the challenges of conventional few-shot TAL methods.