Test-Time Zero-Shot Temporal Action Localization

• URL: http://arxiv.org/abs/2404.05426v2
• Date: Thu, 11 Apr 2024 07:12:35 GMT
• Title: Test-Time Zero-Shot Temporal Action Localization
• Authors: Benedetta Liberatori, Alessandro Conti, Paolo Rota, Yiming Wang, Elisa Ricci,
• Abstract summary: ZS-TAL seeks to identify and locate actions in untrimmed videos unseen during training. Training-based ZS-TAL approaches assume the availability of labeled data for supervised learning. We introduce a novel method that performs Test-Time adaptation for Temporal Action localization (T3AL)
• Score: 58.84919541314969
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model on a large amount of annotated training data. While effective, training-based ZS-TAL approaches assume the availability of labeled data for supervised learning, which can be impractical in some applications. Furthermore, the training process naturally induces a domain bias into the learned model, which may adversely affect the model's generalization ability to arbitrary videos. These considerations prompt us to approach the ZS-TAL problem from a radically novel perspective, relaxing the requirement for training data. To this aim, we introduce a novel method that performs Test-Time adaptation for Temporal Action Localization (T3AL). In a nutshell, T3AL adapts a pre-trained Vision and Language Model (VLM). T3AL operates in three steps. First, a video-level pseudo-label of the action category is computed by aggregating information from the entire video. Then, action localization is performed adopting a novel procedure inspired by self-supervised learning. Finally, frame-level textual descriptions extracted with a state-of-the-art captioning model are employed for refining the action region proposals. We validate the effectiveness of T3AL by conducting experiments on the THUMOS14 and the ActivityNet-v1.3 datasets. Our results demonstrate that T3AL significantly outperforms zero-shot baselines based on state-of-the-art VLMs, confirming the benefit of a test-time adaptation approach.

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