no code implementations • 4 Dec 2023 • Zitong Zhan, Dasong Gao, Yun-Jou Lin, Youjie Xia, Chen Wang
Learning feature correspondence is a foundational task in computer vision, holding immense importance for downstream applications such as visual odometry and 3D reconstruction.
1 code implementation • 16 Nov 2022 • Zitong Zhan, Daniel McKee, Svetlana Lazebnik
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
no code implementations • 10 Mar 2022 • Daniel McKee, Zitong Zhan, Bing Shuai, Davide Modolo, Joseph Tighe, Svetlana Lazebnik
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.