6 papers
MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement
Haoyu Zhang, Jingyi Zhou, Peng Ye +4
With the development of deep learning, ViT-based stereo matching methods have made significant progress due to their remarkable robustness and zero-shot ability. However, due to th…
BRIDGE -- Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation
Dingning Liu, Haoyu Guo, Jingyi Zhou +1
Monocular Depth Estimation (MDE) is a foundational task for computer vision. Traditional methods are limited by data scarcity and quality, hindering their robustness. To overcome t…
Consistency-aware Self-Training for Iterative-based Stereo Matching
Jingyi Zhou, Peng Ye, Haoyu Zhang +6
Iterative-based methods have become mainstream in stereo matching due to their high performance. However, these methods heavily rely on labeled data and face challenges with unlabe…
All-in-One: Transferring Vision Foundation Models into Stereo Matching
Jingyi Zhou, Haoyu Zhang, Jiakang Yuan +5
As a fundamental vision task, stereo matching has made remarkable progress. While recent iterative optimization-based methods have achieved promising performance, their feature ext…
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification
Jiamu Sheng, Jingyi Zhou, Jiong Wang +2
The effectiveness and efficiency of modeling complex spectral-spatial relations are both crucial for Hyperspectral image (HSI) classification. Most existing methods based on CNNs a…
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
Jingyi Zhou, Jiamu Sheng, Jiayuan Fan +4
The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generati…