Effect of Temporal Resolution on Zero-Shot Semantic Understanding of Rapid Human Actions
Overview
This research investigates how temporal resolution affects zero-shot semantic understanding of rapid human actions. Using kendo as a case study, the authors propose a training-free pipeline combining a pre-trained video-language model with large language model-based reasoning. Experiments show that Nearest Class Prototype (NCP) accuracy is highest at 120 Hz, and mean prototype margin improves significantly in the 70-120 Hz range compared to lower frequencies. Secondary experiments indicate that tracking overlays reduce full-observation performance but improve partial-observation performance.
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Reported quantity · video frames per second: 10 other · Basis not reported
“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. Our primary experiment evaluates full-observation raw videos from 10 to 120 Hz in 10-Hz increments. Nearest class prototype (NCP) accuracy is highest at 120 Hz, and the mean over 70-120 Hz is higher than t”
Exact source · revision 2Source owner not reported
Reported quantity · similarity-score points: 2.52 other · Basis not reported
“hat over 10-60 Hz. The corresponding mean prototype margin changes from -5.74 to +2.52 similarity-score points between these two halves and is also highest at 120 Hz. Secondary experiments show that tracking overlays reduce mean full-observation performance but improve mean partial-observation performance. These findings provide preliminary evidence that retaining high-temporal-resolution visual information can benef”
Exact source · revision 2Source owner not reported
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- arXiv Robotics — research abstractsSignalEffect of Temporal Resolution on Zero-Shot Semantic Understanding of Rapid Human Actions
This research investigates how temporal resolution affects zero-shot semantic understanding of rapid human actions. Using kendo as a case study, the authors propose a training-free pipeline combining a pre-trained video-language model with large language model-based reasoning. Experiments show that Nearest Class Prototype (NCP) accuracy is highest at 120 Hz, and mean prototype margin improves significantly in the 70-120 Hz range compared to lower frequencies. Secondary experiments indicate that tracking overlays reduce full-observation performance but improve partial-observation performance.
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