activity
20142023
most citedDiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model

9 citations · 21 across the 12 of their papers we have counts for

collaborators

24 papers

cs.CV2024

CSS-Segment: 2nd Place Report of LSVOS Challenge VOS Track

Jinming Chai, Qin Ma, Junpei Zhang +2

Video object segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this technica…

cs.CV20242 cited

S4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation

Jie Feng, Tianshu Zhang, Junpeng Zhang +4

Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data t…

cs.CV20241 cited

Masked Angle-Aware Autoencoder for Remote Sensing Images

Zhihao Li, Biao Hou, Siteng Ma +4

To overcome the inherent domain gap between remote sensing (RS) images and natural images, some self-supervised representation learning methods have made promising progress. Howeve…

cs.CV2024

Technique Report of CVPR 2024 PBDL Challenges

Ying Fu, Yu Li, Shaodi You +96

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…

cs.CV20241 cited

PVUW 2024 Challenge on Complex Video Understanding: Methods and Results

Henghui Ding, Chang Liu, Yunchao Wei +34

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…

cs.CV2024

V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results

Jiaqi Wang, Yuhang Zang, Pan Zhang +31

Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown…