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

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 susceptible to domain shifts and entail high computational costs. Unlike previous works, we presentFreeZAD, an training-free method for ZSTAD, which leverages image-pretrained vision-language models (VLMs) to directly classify and localize unseen activities within untrimmed videos. 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 VLMs 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, and the adaptation-enhanced extension further narrows the gap with fully supervised approaches. The code are available at:https://github.com/Chaolei98/FreeZAD.