76 citations · 118 across the 16 of their papers we have counts for
16 papers
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Yi Yu, Yufei Wang, Song Xia +4
Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks c…
Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder
Yiyang Ma, Wenhan Yang, Jiaying Liu
The images produced by diffusion models can attain excellent perceptual quality. However, it is challenging for diffusion models to guarantee distortion, hence the integration of d…
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni, Juncheng Wu, Zian Wang +3
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely a…
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
Jialu Sui, Yiyang Ma, Wenhan Yang +3
The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images. However, existing deep-learning (DL)-based Cloud Removal (CR)…
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Yufei Wang, Yi Yu, Wenhan Yang +4
Previous raw image-based low-light image enhancement methods predominantly relied on feed-forward neural networks to learn deterministic mappings from low-light to normally-exposed…
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
Yi Yu, Yufei Wang, Wenhan Yang +3
Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable t…