activity
20142023
most citedSTC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation

619 citations · 3.7k across the 114 of their papers we have counts for

collaborators

119 papers

cs.CV2023

Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos

Haoyuan Li, Haoye Dong, Hanchao Jia +4

Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. However, existing…

cs.CV2023★ 3 cited

CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation

Hongguang Zhu, Yunchao Wei, Xiaodan Liang +2

Vision-Language Pretraining (VLP) has shown impressive results on diverse downstream tasks by offline training on large-scale datasets. Regarding the growing nature of real-world d…

cs.CV2023

LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts

Binbin Yang, Yi Luo, Ziliang Chen +3

Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis. Prior works mostly rely on pre-trained linguistic models, but a t…

cs.CV2022★ 4 cited

Structure-Preserving 3D Garment Modeling with Neural Sewing Machines

Xipeng Chen, Guangrun Wang, Dizhong Zhu +3

3D Garment modeling is a critical and challenging topic in the area of computer vision and graphics, with increasing attention focused on garment representation learning, garment r…

cs.CV2022★ 11 cited

Modern Augmented Reality: Applications, Trends, and Future Directions

Shervin Minaee, Xiaodan Liang, Shuicheng Yan

Augmented reality (AR) is one of the relatively old, yet trending areas in the intersection of computer vision and computer graphics with numerous applications in several areas, fr…

cs.CV2021★ 19 cited

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN

Zhenyu Xie, Zaiyu Huang, Fuwei Zhao +3

Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential. Yet, as most try-on approaches fi…