Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

• URL: http://arxiv.org/abs/2501.13795v1
• Date: Thu, 23 Jan 2025 16:13:58 GMT
• Title: Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models
• Authors: Chaolei Han, Hongsong Wang, Jidong Kuang, Lei Zhang, Jie Gui,
• Abstract summary: We propose a training-Free Zero-shot temporal Action Detection (FreeZAD) method. We leverage existing vision-language (ViL) models to directly classify and localize unseen activities within untrimmed videos. Our training-free method outperforms state-of-the-art unsupervised methods while requiring only 1/13 of the runtime.
• Score: 15.17499718666202
• License:
• Abstract: Existing zero-shot temporal action detection (ZSTAD) methods predominantly use fully supervised or unsupervised strategies to recognize unseen activities. However, these training-based methods are prone to domain shifts and require high computational costs, which hinder their practical applicability in real-world scenarios. In this paper, unlike previous works, we propose a training-Free Zero-shot temporal Action Detection (FreeZAD) method, leveraging existing vision-language (ViL) models to directly classify and localize unseen activities within untrimmed videos without any additional fine-tuning or adaptation. We mitigate the need for explicit temporal modeling and reliance on pseudo-label quality by designing the LOGarithmic decay weighted Outer-Inner-Contrastive Score (LogOIC) and frequency-based Actionness Calibration. Furthermore, we introduce a test-time adaptation (TTA) strategy using Prototype-Centric Sampling (PCS) to expand FreeZAD, enabling ViL models to adapt more effectively for ZSTAD. Extensive experiments on the THUMOS14 and ActivityNet-1.3 datasets demonstrate that our training-free method outperforms state-of-the-art unsupervised methods while requiring only 1/13 of the runtime. When equipped with TTA, the enhanced method further narrows the gap with fully supervised methods.

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