85 citations · 151 across the 9 of their papers we have counts for
7 papers · 1 filter
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Tong Wu, Jiarui Zhang, Xiao Fu +9
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, re…
Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting
Yu Qiao, Ziqi Wei, Yuhao Liu +4
This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting. Many approaches achieve alpha mattes with complex en…
Hierarchical and Progressive Image Matting
Yu Qiao, Yuhao Liu, Ziqi Wei +4
Most matting researches resort to advanced semantics to achieve high-quality alpha mattes, and direct low-level features combination is usually explored to complement alpha details…
Progressive Glass Segmentation
Letian Yu, Haiyang Mei, Wen Dong +4
Glass is very common in the real world. Influenced by the uncertainty about the glass region and the varying complex scenes behind the glass, the existence of glass poses severe ch…
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
Yu Qiao, Jincheng Zhu, Chengjiang Long +4
Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotat…
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jiqing Zhang, Kai Zhao, Bo Dong +4
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effective…
Multi-scale Information Assembly for Image Matting
Yu Qiao, Yuhao Liu, Qiang Zhu +4
Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images. We argue that the foregrou…