no code implementations • 28 Nov 2023 • Lingteng Qiu, GuanYing Chen, Xiaodong Gu, Qi Zuo, Mutian Xu, Yushuang Wu, Weihao Yuan, Zilong Dong, Liefeng Bo, Xiaoguang Han
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
no code implementations • ICCV 2023 • Yushuang Wu, Xiao Li, Jinglu Wang, Xiaoguang Han, Shuguang Cui, Yan Lu
Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF.
no code implementations • 7 Jul 2023 • Zizheng Yan, Yushuang Wu, Yipeng Qin, Xiaoguang Han, Shuguang Cui, Guanbin Li
In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i. e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain.
1 code implementation • CVPR 2023 • Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei, Xiao Li, Guanbin Li, Yihao Li, Shuguang Cui, Xiaoguang Han
Thus, we propose a new task, SCoDA, for the domain adaptation of real scan shape completion from synthetic data.
no code implementations • CVPR 2023 • Xianggang Yu, Mutian Xu, Yidan Zhang, Haolin Liu, Chongjie Ye, Yushuang Wu, Zizheng Yan, Chenming Zhu, Zhangyang Xiong, Tianyou Liang, GuanYing Chen, Shuguang Cui, Xiaoguang Han
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
no code implementations • 27 Sep 2022 • Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, Yizhou Yu
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e. g.,} social network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
no code implementations • 23 Aug 2022 • Zhangyang Xiong, Dong Du, Yushuang Wu, Jingqi Dong, Di Kang, Linchao Bao, Xiaoguang Han
On synthetic data, our Intersection-Over-Union (IoU) achieves to 93. 5%, 18% higher compared with PIFuHD.
1 code implementation • 9 May 2022 • Zizheng Yan, Yushuang Wu, Guanbin Li, Yipeng Qin, Xiaoguang Han, Shuguang Cui
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
Ranked #1 on Semi-supervised Domain Adaptation on VisDA2017
no code implementations • 22 Feb 2022 • Yushuang Wu, Zizheng Yan, Shengcai Cai, Guanbin Li, Yizhou Yu, Xiaoguang Han, Shuguang Cui
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.
Representation Learning Weakly supervised Semantic Segmentation +1
5 code implementations • 15 Nov 2021 • Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, Liwei Wu
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #20 on Anomaly Detection on MVTec AD
Unsupervised Anomaly Detection Weakly Supervised Defect Detection
no code implementations • ACM International Conference on Multimedia 2021 • Zizheng Yan, Xianggang Yu, Yipeng Qin, Yushuang Wu, Xiaoguang Han, Shuguang Cui
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
no code implementations • CVPR 2021 • Yuda Qiu, Xiaojie Xu, Lingteng Qiu, Yan Pan, Yushuang Wu, Weikai Chen, Xiaoguang Han
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
no code implementations • ICCV 2021 • Yushuang Wu, Zizheng Yan, Xiaoguang Han, Guanbin Li, Changqing Zou, Shuguang Cui
The key point of language-guided person search is to construct the cross-modal association between visual and textual input.