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ZAIWANG GU

7 accepted papers

2026

Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning

ICLR 2026poster

Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but it is susceptible to local minima and noise overfitting. Although DMs can provide strong priors for Bayesian approaches,…

Cited by 0SourceScholar
2026

SURE: Semi-Dense Uncertainty-REfined Feature Matching

ICRA 2026poster

Establishing reliable image correspondences is essential for many robotic vision problems. However, existing methods often struggle in challenging scenarios with large viewpoint changes or textureless regions, where incorrect correspondences may still receive high similarity scores. This is mainly b…

2025

Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching

ICLR 2025poster

Dense feature matching methods aim to estimate a dense correspondence field between images. Inaccurate correspondence can occur due to the presence of unmatchable region, necessitating the need for certainty measurement. This is typically addressed by training a binary classifier to decide whether e…

Cited by 0SourcePDFScholar
2024

Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching

CVPR 2024poster

Geometric knowledge has been shown to be beneficial for the stereo matching task. However prior attempts to integrate geometric insights into stereo matching algorithms have largely focused on geometric knowledge from single images while crucial cross-view factors such as occlusion and matching uniq…

2024

SuperJunction: Learning-Based Junction Detection for Retinal Image Registration

AAAI 2024technical

Keypoints-based approaches have shown to be promising for retinal image registration, which superimpose two or more images from different views based on keypoint detection and description. However, existing approaches suffer from ineffective keypoint detector and descriptor training. Meanwhile, the…

2020

Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

ECCV 2020poster

Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions, by only leveraging normal images in training phase. Normal images from healthy subjects often have regular structures (e.g., the structured blood vessels in the fundus image, or…