1 code implementation • 28 Mar 2024 • Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Mubarak Shah, Ajmal Mian
Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining consistent object segmentation due to temporal context variability and the presence of other visually similar objects.
Ranked #3 on Referring Video Object Segmentation on Refer-YouTube-VOS (using extra training data)
no code implementations • 21 Mar 2024 • Zijie Wu, Mingtao Feng, Yaonan Wang, He Xie, Weisheng Dong, Bo Miao, Ajmal Mian
Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes.
1 code implementation • ICCV 2023 • Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
To address the drift problem, we propose a Spectrum-guided Multi-granularity (SgMg) approach, which performs direct segmentation on the encoded features and employs visual details to further optimize the masks.
Ranked #1 on Referring Expression Segmentation on J-HMDB (using extra training data)
no code implementations • 21 Jul 2022 • Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory.
Ranked #16 on Semi-Supervised Video Object Segmentation on DAVIS 2017 (val) (using extra training data)
1 code implementation • 1 Aug 2021 • Bo Miao, Liguang Zhou, Ajmal Mian, Tin Lun Lam, Yangsheng Xu
The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network.
1 code implementation • 27 Jul 2021 • Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
We propose a self-supervised spatio-temporal matching method, coined Motion-Aware Mask Propagation (MAMP), for video object segmentation.