Whareformer: Learning to Track What is Where in Long Egocentric Videos
The recently established 'Out of Sight, Not out of Mind' (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wearer, online, maintaining knowledge of instance locations throughout the video even when they leave the field of view or become heavily occluded. In this paper, we propose the first learning-based solution to the OSNOM task: Whareformer, a transformer-based model with two components: an updatable memory of established tracks and a track assignment module that associates observations with existing tracks in a feed-forward manner. Whareformer join
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorJacob Chalk →
Whareformer: Learning to Track What is Where in Long Egocentric Videos
- Linked via arxiv authorSaptarshi Sinha →
Whareformer: Learning to Track What is Where in Long Egocentric Videos
- Linked via arxiv authorDima Damen →
Whareformer: Learning to Track What is Where in Long Egocentric Videos
- Linked via arxiv authorYannis Kalantidis →
Whareformer: Learning to Track What is Where in Long Egocentric Videos
- Linked via arxiv authorDiane Larlus →
Whareformer: Learning to Track What is Where in Long Egocentric Videos