3 accepted papers
"In this study, we investigate the task of active testing for label-efficient evaluation, which aims to estimate a model’s performance on an unlabeled test dataset with a limited annotation budget. Previous approaches relied on deep ensemble models to identify highly informative instances for labeli…
"We present MoE-DiffIR, an innovative universal compressed image restoration (CIR) method with task-customized diffusion priors. This intends to handle two pivotal challenges in the existing CIR methods: (i) lacking adaptability and universality for different image codecs, , JPEG and WebP; (ii) poor…
"Compressed Image Super-resolution (CSR) aims to simultaneously super-resolve the compressed images and tackle the challenging hybrid distortions caused by compression. However, existing works on CSR usually focus on single compression codec, , JPEG, ignoring the diverse traditional or learning-base…