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Found 3 papers, 1 papers with code
iMatching: Imperative Correspondence Learning
Learning feature correspondence is a foundational task in computer vision, holding immense importance for downstream applications such as visual odometry and 3D reconstruction.
Robust Online Video Instance Segmentation with Track Queries
We propose a fully online transformer-based video instance segmentation model that performs comparably to top offline methods on the YouTube-VIS 2019 benchmark and considerably outperforms them on UVO and OVIS.
Ranked #13 on
Video Instance Segmentation
on OVIS validation
Transfer of Representations to Video Label Propagation: Implementation Factors Matter
This work studies feature representations for dense label propagation in video, with a focus on recently proposed methods that learn video correspondence using self-supervised signals such as colorization or temporal cycle consistency.
