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.

本论文介绍了一种新颖的少样本学习中的时间动作定位 (TAL) 方法，通过在实际视频中泛化不同情境的能力来解决常规单提示学习方法存在的过拟合问题。采用多提示学习框架，增强了每个动作的一组多样化提示，更有效地捕捉通用特征并减少过拟合风险。此外，利用最优输运理论，高效地将这些提示与动作特征对齐，优化了适应视频数据多方面特性的综合表示。实验证明了我们提出的多提示最优输运方法在 THUMOS-14 和 EpicKitchens100 的标准数据集上能够显著提高少样本 TAL 方法的定位准确性和鲁棒性，突出了其有效性。