1 code implementation • 19 Oct 2023 • Ho Kei Cheng, Seoung Wug Oh, Brian Price, Joon-Young Lee, Alexander Schwing
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentation result.
Ranked #1 on Semi-Supervised Video Object Segmentation on MOSE
1 code implementation • ICCV 2023 • Ho Kei Cheng, Seoung Wug Oh, Brian Price, Alexander Schwing, Joon-Young Lee
To 'track anything' without training on video data for every individual task, we develop a decoupled video segmentation approach (DEVA), composed of task-specific image-level segmentation and class/task-agnostic bi-directional temporal propagation.
Ranked #1 on Unsupervised Video Object Segmentation on DAVIS 2016 val (using extra training data)
Open-Vocabulary Video Segmentation Open-World Video Segmentation +7
1 code implementation • 14 Jul 2022 • Ho Kei Cheng, Alexander G. Schwing
We present XMem, a video object segmentation architecture for long videos with unified feature memory stores inspired by the Atkinson-Shiffrin memory model.
Ranked #1 on Video Object Segmentation on YouTube-VOS 2019 (using extra training data)
3 code implementations • NeurIPS 2021 • Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang
This paper presents a simple yet effective approach to modeling space-time correspondences in the context of video object segmentation.
Ranked #7 on Video Object Segmentation on YouTube-VOS 2019
Semantic Segmentation Semi-Supervised Video Object Segmentation +1
5 code implementations • CVPR 2021 • Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.
Ranked #1 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
Interactive Video Object Segmentation Semantic Segmentation +2
2 code implementations • CVPR 2020 • Ho Kei Cheng, Jihoon Chung, Yu-Wing Tai, Chi-Keung Tang
In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.
Ranked #1 on Semantic Segmentation on BIG (using extra training data)