Egocentric zone-aware action recognition across environments
Simone Alberto Peirone, Gabriele Goletto, Mirco Planamente, Andrea, Bottino, Barbara Caputo, Giuseppe Averta

TL;DR
This paper proposes a method to improve egocentric action recognition by decoupling scene-specific zone appearances from universal zone representations, enhancing cross-domain transferability in diverse environments.
Contribution
It introduces a novel approach to separate domain-specific zone appearance from universal features, boosting transferability of EAR models across different environments.
Findings
Improved cross-domain action recognition accuracy
Effective decoupling of scene-specific and universal zone features
Validated on EPIC-Kitchens-100 and Argo1M datasets
Abstract
Human activities exhibit a strong correlation between actions and the places where these are performed, such as washing something at a sink. More specifically, in daily living environments we may identify particular locations, hereinafter named activity-centric zones, which may afford a set of homogeneous actions. Their knowledge can serve as a prior to favor vision models to recognize human activities. However, the appearance of these zones is scene-specific, limiting the transferability of this prior information to unfamiliar areas and domains. This problem is particularly relevant in egocentric vision, where the environment takes up most of the image, making it even more difficult to separate the action from the context. In this paper, we discuss the importance of decoupling the domain-specific appearance of activity-centric zones from their universal, domain-agnostic…
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Taxonomy
TopicsHuman Pose and Action Recognition · Gait Recognition and Analysis · Anomaly Detection Techniques and Applications
MethodsSparse Evolutionary Training
