4 papers
All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network
Bingnan Wang, Bin Qin, Jiangmeng Li +3
Image restoration represents a promising approach for addressing the inherent defects of image content distortion. Standard image restoration approaches suffer from high storage co…
AmPLe: Supporting Vision-Language Models via Adaptive-Debiased Ensemble Multi-Prompt Learning
Fei Song, Yi Li, Jiangmeng Li +4
Multi-prompt learning methods have emerged as an effective approach for facilitating the rapid adaptation of vision-language models to downstream tasks with limited resources. Exis…
BayesTTA: Continual-Temporal Test-Time Adaptation for Vision-Language Models via Gaussian Discriminant Analysis
Shuang Cui, Jinglin Xu, Yi Li +6
Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but degrade significantly under \textit{temporally evolving distribution shifts} common in real-worl…
Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Shuang Cui, Yi Li, Jiangmeng Li +4
Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation…