SOURCE-LINKED INTELLIGENCE

Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

We propose a novel pretraining strategy for skeleton-based zero-shot spatio-temporal action localization to estimate unseen actions for person instances while overcoming high annotation costs for training via new target actions and pretraining using large-scale action scenery datasets. Specifically, our approach, termed Skeleton-Language feature Pooling Switching, introduces a weakly-supervised vision-language pretraining mechanism. This mechanism transitions pooling kernels from pretraining, which aggregates skeleton features at the video level and aligns them with each video's known action t

Read original source ↗ Open in workspace

recordType

paper

region

Global