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Jinlan Xu

3 accepted papers

2025

Q-Norm: Robust Representation Learning via Quality-Adaptive Normalization

ICCV 2025poster

Although deep neural networks have achieved remarkable success in various computer vision tasks, they face significant challenges in degraded image understanding due to domain shifts caused by quality variations. Drawing biological inspiration from the human visual system (HVS), which dynamically ad…

2025

QuARF: Quality-Adaptive Receptive Fields for Degraded Image Perception

AAAI 2025technical

Advanced Deep Neural Networks (DNNs) perform well for high-quality images, but their performance dramatically decreases for degraded images. Data augmentation is commonly used to alleviate this problem, but using too much perturbed data might seriously decrease the performance on pristine images. To…