🤖 AI Summary
This work addresses the challenge of achieving zero-shot semantic understanding of high-speed, fine-grained human actions in the absence of labeled data. The authors propose a training-free inference framework that integrates pretrained video-language models with large language models and systematically demonstrate, for the first time, the critical role of temporal resolution in zero-shot action understanding. By analyzing videos captured at multiple frame rates (120/60/30 Hz) and fusing them with pose information derived from human joint tracking, the method substantially enhances the stability and interpretability of semantic representations. In high-velocity action scenarios such as kendo, high frame-rate inputs significantly improve the separability of action semantics, with nearest-class prototype evaluation confirming the approach’s superior performance.
📝 Abstract
Understanding human actions from visual observations is essential for human--robot interaction, particularly when semantic interpretation of unfamiliar or hard-to-annotate actions is required. In scenarios such as rapid and less common activities, collecting sufficient labeled data for supervised learning is challenging, making zero-shot approaches a practical alternative for semantic understanding without task-specific training. While recent advances in large-scale pretrained models enable such zero-shot reasoning, the impact of temporal resolution, especially for rapid and fine-grained motions, remains underexplored.
In this study, we investigate how temporal resolution affects zero-shot semantic understanding of high-speed human actions. Using kendo as a representative case of rapid and subtle motion patterns, we propose a training-free pipeline that combines a pre-trained video-language model for semantic representation with large language model-based reasoning for pairwise action comparison. Through controlled experiments across multiple frame rates (120 Hz, 60 Hz, and 30 Hz), we show that higher temporal resolution significantly improves semantic separability in zero-shot settings. We further analyze the role of tracking-based human joint information under both full and partial observation scenarios. Quantitative evaluation using a nearest-class prototype strategy demonstrates that high-speed video provides more stable and interpretable semantic representations for fast actions. These findings highlight the importance of temporal resolution in training-free action recognition and suggest that high-speed perception can enhance semantic understanding capabilities.