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Title: Human Hands as Probes for Interactive Object Understanding
Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.  more » « less
Award ID(s):
2007035
NSF-PAR ID:
10416321
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Page Range / eLocation ID:
3283 to 3293
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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