Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes

• URL: http://arxiv.org/abs/2510.27255v2
• Date: Mon, 03 Nov 2025 07:33:58 GMT
• Title: Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes
• Authors: Yehna Kim, Young-Eun Kim, Seong-Whan Lee,
• Abstract summary: We propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords.<n>This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation.<n>In our zero-shot experiments, our model achieves accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively.
• Score: 54.50887214639301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities in zero-shot action recognition by learning to associate video embeddings with class embeddings. However, a significant challenge arises when relying solely on action classes to provide semantic context, particularly due to the presence of multi-semantic words, which can introduce ambiguity in understanding the intended concepts of actions. To address this issue, we propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords. This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation. Additionally, we introduce a spatio-temporal interaction module designed to focus on objects and action units, facilitating alignment between description attributes and video content. In our zero-shot experiments, our model achieves impressive results, attaining accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively, underscoring the model's adaptability and effectiveness across various downstream tasks.

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