Jinkun You — accepted AI-conference papers

2026

Dense Cross-Scale Image Alignment with Fully Spatial Correlation and Just Noticeable Difference Guidance

Jinkun You, Jiaxue Li, Jie Zhang, Yicong Zhou

AAAI 2026technical

Existing unsupervised image alignment methods exhibit limited accuracy and high computational complexity. To address these challenges, we propose a dense cross-scale image alignment model. It takes into account the correlations between cross-scale features to decrease the alignment difficulty. Our m

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2026

Towards Generalized Multimodal Homography Estimation

Jinkun You, Jiaxin Cheng, Jie Zhang, Yicong Zhou

CVPR 2026

Supervised and unsupervised homography estimation methods depend on image pairs tailored to specific modalities to achieve high accuracy. However, their performance deteriorates substantially when applied to unseen modalities. To address this issue, we propose a training data synthesis method that g

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