1 citations · 2 across the 4 of their papers we have counts for
7 papers
Transformer based Pluralistic Image Completion with Reduced Information Loss
Qiankun Liu, Yuqi Jiang, Zhentao Tan +5
Transformer based methods have achieved great success in image inpainting recently. However, we find that these solutions regard each pixel as a token, thus suffering from an infor…
HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending
Tianyi Wei, Dongdong Chen, Wenbo Zhou +4
Hair editing has made tremendous progress in recent years. Early hair editing methods use well-drawn sketches or masks to specify the editing conditions. Even though they can enabl…
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting
Qidong Huang, Xiaoyi Dong, Dongdong Chen +5
In this paper, we investigate the adversarial robustness of vision transformers that are equipped with BERT pretraining (e.g., BEiT, MAE). A surprising observation is that MAE has…
HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
Qinhong Yang, Dongdong Chen, Zhentao Tan +6
This paper introduces a new large-scale image restoration dataset, called HQ-50K, which contains 50,000 high-quality images with rich texture details and semantic diversity. We ana…
Designing a Better Asymmetric VQGAN for StableDiffusion
Zixin Zhu, Xuelu Feng, Dongdong Chen +5
StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion mo…
Diversity-Aware Meta Visual Prompting
Qidong Huang, Xiaoyi Dong, Dongdong Chen +4
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.…