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

• URL: http://arxiv.org/abs/2403.18915v2
• Date: Thu, 24 Jul 2025 15:19:06 GMT
• Title: PLOT-TAL: Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization
• Authors: Edward Fish, Andrew Gilbert,
• Abstract summary: We introduce PLOT-TAL, a framework that finds globally optimal alignment between the prompt ensemble and the video's temporal features.<n>Our method establishes a new state-of-the-art on the challenging few-shot benchmarks of THUMOS'14 and EPIC-Kitchens, without requiring complex meta-learning.
• Score: 8.173421927978117
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Few-shot temporal action localization (TAL) methods that adapt large models via single-prompt tuning often fail to produce precise temporal boundaries. This stems from the model learning a non-discriminative mean representation of an action from sparse data, which compromises generalization. We address this by proposing a new paradigm based on multi-prompt ensembles, where a set of diverse, learnable prompts for each action is encouraged to specialize on compositional sub-events. To enforce this specialization, we introduce PLOT-TAL, a framework that leverages Optimal Transport (OT) to find a globally optimal alignment between the prompt ensemble and the video's temporal features. Our method establishes a new state-of-the-art on the challenging few-shot benchmarks of THUMOS'14 and EPIC-Kitchens, without requiring complex meta-learning. The significant performance gains, particularly at high IoU thresholds, validate our hypothesis and demonstrate the superiority of learning distributed, compositional representations for precise temporal localization.

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