activity
20222024
most citedDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data

23 citations · 46 across the 11 of their papers we have counts for

collaborators

8 papers

cs.CV20237 cited

FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Lihe Yang, Xiaogang Xu, Bingyi Kang +2

Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations t…

cs.CV20235 cited

Low-Light Image Enhancement via Structure Modeling and Guidance

Xiaogang Xu, Ruixing Wang, Jiangbo Lu

This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to…

cs.CV2023

Leaf Cultivar Identification via Prototype-enhanced Learning

Yiyi Zhang, Zhiwen Ying, Ying Zheng +5

Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years. Due to the high similarity…

cs.CV20234 cited

TriVol: Point Cloud Rendering via Triple Volumes

Tao Hu, Xiaogang Xu, Ruihang Chu +1

Existing learning-based methods for point cloud rendering adopt various 3D representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. Howeve…

cs.CV20232 cited

Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields

Tao Hu, Xiaogang Xu, Shu Liu +1

Synthesizing photo-realistic images from a point cloud is challenging because of the sparsity of point cloud representation. Recent Neural Radiance Fields and extensions are propos…

cs.CV20221 cited

DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation

Xin Lai, Zhuotao Tian, Xiaogang Xu +5

Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset…