most citedS4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

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.CV2024

3D Object Detection from Point Cloud via Voting Step Diffusion

Haoran Hou, Mingtao Feng, Zijie Wu +4

3D object detection is a fundamental task in scene understanding. Numerous research efforts have been dedicated to better incorporate Hough voting into the 3D object detection pipe…

cs.CV20241 cited

SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images

Jie Feng, Hao Huang, Junpeng Zhang +3

Mainstreamed weakly supervised road extractors rely on highly confident pseudo-labels propagated from scribbles, and their performance often degrades gradually as the image scenes…

cs.CV2024

Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement

Huachen Fang, Jinjian Wu, Qibin Hou +2

Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In t…

cs.CV2021

Robust Depth Completion with Uncertainty-Driven Loss Functions

Yufan Zhu, Weisheng Dong, Leida Li +3

Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixe…