Abstract
Humans reliably surpass the performance of the most advanced AI models in action recognition, especially in realworld scenarios with low resolution, occlusions, and visual clutter. These models are somewhat similar to humans in using architecture that allows hierarchical feature extraction. However, they prioritise different features, leading to notable differences in their recognition. This study investigated these differences by introducing Epic ReduAct 1, a dataset derived from Epic-Kitchens-100. It consists of Easy and Hard ego-centric videos across various action classes. Critically, our dataset incorporates the concepts of Minimal Recognisable Configuration (MIRC) and sub-MIRC derived by progressively reducing the spatial content of the action videos across multiple stages. This enables a controlled evaluation of recognition difficulty for humans and AI models. This study examines the fundamental differences between human and AI recognition processes. While humans, unlike AI models, demonstrate proficiency in recognising hard videos, they experience a sharp decline in recognition ability as visual information is reduced, ultimately reaching a threshold beyond which recognition is no longer possible. In contrast, the AI models examined in this study appeared to exhibit greater resilience within this specific context, with recognition confidence decreasing gradually or, in some cases, even increasing at later reduction stages. These findings suggest that the limitations observed in human recognition do not directly translate to AI models, highlighting the distinct nature of their processing mechanisms.