3 citations · 9 across the 6 of their papers we have counts for
8 papers
MacFormer: Semantic Segmentation with Fine Object Boundaries
Guoan Xu, Wenfeng Huang, Tao Wu +5
Semantic segmentation involves assigning a specific category to each pixel in an image. While Vision Transformer-based models have made significant progress, current semantic segme…
HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation
Guoan Xu, Wenjing Jia, Tao Wu +2
Both Convolutional Neural Networks (CNNs) and Transformers have shown great success in semantic segmentation tasks. Efforts have been made to integrate CNNs with Transformer models…
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
Wenjie Li, Mei Wang, Kai Zhang +6
Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep…
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Juncheng Li, Bodong Cheng, Ying Chen +2
Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatl…
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Hansheng Guo, Juncheng Li, Guangwei Gao +2
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous m…
Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
Guoan Xu, Juncheng Li, Guangwei Gao +3
In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to captu…