no code implementations • 19 Aug 2021 • Daniel McKee, Bing Shuai, Andrew Berneshawi, Manchen Wang, Davide Modolo, Svetlana Lazebnik, Joseph Tighe
Next, to tackle harder tracking cases, we mine hard examples across an unlabeled pool of real videos with a tracker trained on our hallucinated video data.
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.
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 • CVPR 2023 • Daniel McKee, Justin Salamon, Josef Sivic, Bryan Russell
A key challenge of this problem setting is that existing music video datasets provide the needed (video, music) training pairs, but lack text descriptions of the music.
1 code implementation • ICCV 2021 • Aiyu Cui, Daniel McKee, Svetlana Lazebnik
We propose a flexible person generation framework called Dressing in Order (DiOr), which supports 2D pose transfer, virtual try-on, and several fashion editing tasks.
Ranked #1 on Pose Transfer on Deep-Fashion