activity
20242026
most citedMoCha-Stereo: Motif Channel Attention Network for Stereo Matching

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

StereoVGGT: A Training-Free Visual Geometry Transformer for Stereo Vision

Ziyang Chen, Yansong Qu, You Shen +2

Driven by the advancement of 3D devices, stereo vision tasks including stereo matching and stereo conversion have emerged as a critical research frontier. Contemporary stereo visio…

cs.CV2025

SplitFlux: Learning to Decouple Content and Style from a Single Image

Yitong Yang, Yinglin Wang, Changshuo Wang +3

Disentangling image content and style is essential for customized image generation. Existing SDXL-based methods struggle to achieve high-quality results, while the recently propose…

cs.CV2025★ 1 cited

Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer

Ziyang Chen, Wenting Li, Yongjun Zhang +4

Constrained by the low-rank bottleneck inherent in attention mechanisms, current stereo matching transformers suffer from limited nonlinear expressivity, which renders their featur…

cs.CV2024★ 2 cited

Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph

Ziyang Chen, Yongjun Zhang, Wenting Li +3

Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inheren…

cs.CV2024★ 3 cited

MoCha-Stereo: Motif Channel Attention Network for Stereo Matching

Ziyang Chen, Wei Long, He Yao +4

Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel gen…