4 citations · 9 across the 8 of their papers we have counts for
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A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
Minghao Zhou, Hong Wang, Yefeng Zheng +1
Feature upsampling is a fundamental and indispensable ingredient of almost all current network structures for dense prediction tasks. Recently, a popular similarity-based feature u…
TRG-Net: An Interpretable and Controllable Rain Generator
Zhiqiang Pang, Hong Wang, Qi Xie +2
Exploring and modeling rain generation mechanism is critical for augmenting paired data to ease training of rainy image processing models. Against this task, this study proposes a…
Interactive Segmentation as Gaussian Process Classification
Minghao Zhou, Hong Wang, Qian Zhao +4
Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly foll…
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition
Jiayin Sun, Hong Wang, Qiulei Dong
Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision communi…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically co…
From Rain Generation to Rain Removal
Hong Wang, Zongsheng Yue, Qi Xie +3
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most o…