A skeleton-based zero-shot VAD method distills LLM knowledge for action typicality during training and performs test-time context uniqueness analysis to derive scene-adaptive normality boundaries, claiming SOTA results on four datasets with over 100 unseen scenes.
Spatial temporal graph convolutional networks for skeleton-based action recognition
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iPay fuses RGB and skeleton expert streams via dual-attention and a prior-driven Spatial Difference Discriminator to reach 83.45% accuracy on 500+ real-world payment clips from onboard transit cameras.
Extends Social-STGCNN with CVAE for multimodal trajectory prediction and reports moderate gains plus better diversity on ETH/UCY benchmarks and robot data.
citing papers explorer
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Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection
A skeleton-based zero-shot VAD method distills LLM knowledge for action typicality during training and performs test-time context uniqueness analysis to derive scene-adaptive normality boundaries, claiming SOTA results on four datasets with over 100 unseen scenes.
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iPay: Integrated Payment Action Recognition via Multimodal Networks and Adaptive Spatial Prior Learning
iPay fuses RGB and skeleton expert streams via dual-attention and a prior-driven Spatial Difference Discriminator to reach 83.45% accuracy on 500+ real-world payment clips from onboard transit cameras.
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On Improving Multimodal Pedestrian Trajectory Prediction with CVAE: A Study on Benchmark and Robot Data
Extends Social-STGCNN with CVAE for multimodal trajectory prediction and reports moderate gains plus better diversity on ETH/UCY benchmarks and robot data.