AI RESEARCH
Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations
arXiv CS.CV
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ArXi:2603.08317v1 Announce Type: new 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. Understanding the sources of this performance gap is essential for developing robust and human-aligned models. In this paper, we present a large-scale human-AI comparative study of egocentric action recognition using Minimal Identifiable Recognition Crops (MIRCs), defined as the smallest spatial or spatiotemporal regions sufficient for reliable human recognition.