Papers
1–4 of 4Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations
Humans consistently outperform state-of-the-art AI models in action recognition, particularly in challenging real-world conditions involving low resolution, occlusion, and visual clutter. Understandin...
Novel Semantic Prompting for Zero-Shot Action Recognition
Zero-shot action recognition relies on transferring knowledge from vision-language models to unseen actions using semantic descriptions. While recent methods focus on temporal modeling or architectura...
Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models
Recent advances in large-scale pretrained vision models have demonstrated impressive capabilities across a wide range of downstream tasks, including cross-modal and multi-modal scenarios. However, the...
M3GCLR: Multi-View Mini-Max Infinite Skeleton-Data Game Contrastive Learning For Skeleton-Based Action Recognition
In recent years, contrastive learning has drawn significant attention as an effective approach to reducing reliance on labeled data. However, existing methods for self-supervised skeleton-based action...