5 papers
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
Retina gap junctions support the robust perception by warping neural representational geometries along the visual hierarchy
Yang Yue, Shenjian Zhang, Yonghong Tian +2
Deep Neural Networks (DNNs) are vulnerable to elaborately designed adversarial noise, although they have achieved extraordinary success in many tasks. Compared with DNNs, the human…
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +3
Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image il…
Task-driven Image Fusion with Learnable Fusion Loss
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +5
Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual features compared to single-source images, often impr…
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms fa…