19 citations · 38 across the 7 of their papers we have counts for
7 papers
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
Yuang Ai, Xiaoqiang Zhou, Huaibo Huang +4
Image restoration (IR) in real-world scenarios presents significant challenges due to the lack of high-capacity models and comprehensive datasets. To tackle these issues, we presen…
Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration
Yuang Ai, Huaibo Huang, Xiaoqiang Zhou +2
Despite substantial progress, all-in-one image restoration (IR) grapples with persistent challenges in handling intricate real-world degradations. This paper introduces MPerceiver:…
Lightweight Vision Transformer with Bidirectional Interaction
Qihang Fan, Huaibo Huang, Xiaoqiang Zhou +1
Recent advancements in vision backbones have significantly improved their performance by simultaneously modeling images' local and global contexts. However, the bidirectional inter…
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
Yuang Ai, Xiaoqiang Zhou, Huaibo Huang +2
Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) by accessing both the source and target data. Considering p…
Vision Transformer with Super Token Sampling
Huaibo Huang, Xiaoqiang Zhou, Jie Cao +2
Vision transformer has achieved impressive performance for many vision tasks. However, it may suffer from high redundancy in capturing local features for shallow layers. Local self…
Contrastive Attention Network with Dense Field Estimation for Face Completion
Xin Ma, Xiaoqiang Zhou, Huaibo Huang +3
Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations…
Free-Form Image Inpainting via Contrastive Attention Network
Xin Ma, Xiaoqiang Zhou, Huaibo Huang +3
Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representa…