Egocentric zone-aware action recognition across environments
This work addresses a domain-specific challenge in egocentric vision for improving action recognition in unfamiliar environments, representing an incremental advance.
The paper tackles the problem of cross-domain transferability in egocentric action recognition by decoupling domain-specific appearance from universal representations of activity-centric zones, achieving improved performance on EPIC-Kitchens-100 and Argo1M datasets.
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 representations, and show how the latter can improve the cross-domain transferability of Egocentric Action Recognition (EAR) models. We validate our solution on the EPIC-Kitchens-100 and Argo1M datasets