31 citations · 71 across the 18 of their papers we have counts for
18 papers
Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration
Xinkai Lu, Wenjun Chen, Yi Li +2
Infrared small object detection has made significant progress in recent years. However, degradations such as fog and nonuniformity can suppress target-background contrast, substant…
Powerful Lossy Compression for Noisy Images
Shilv Cai, Xiaoguo Liang, Shuning Cao +4
Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categ…
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
Yun Guo, Xueyao Xiao, Yi Chang +2
Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real ima…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence
Shengqi Xu, Shuning Cao, Haoyue Liu +3
In this technical report, we briefly introduce the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG+ Track 2.2. In t…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence
Shengqi Xu, Xueyao Xiao, Shuning Cao +2
In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of the CVPR 2023 UG+ chal…
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG+ Track 3
Yun Guo, Xueyao Xiao, Xiaoxiong Wang +3
In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG+ Track 3. In this task, we propose an efficient two-…
Make Lossy Compression Meaningful for Low-Light Images
Shilv Cai, Liqun Chen, Sheng Zhong +3
Low-light images frequently occur due to unavoidable environmental influences or technical limitations, such as insufficient lighting or limited exposure time. To achieve better vi…
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
Hanyu Zhou, Yi Chang, Gang Chen +1
Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the doma…
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
Hanyu Zhou, Yi Chang, Wending Yan +1
Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing method…
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Yi Chang, Yun Guo, Yuntong Ye +5
Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less ge…
High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
Shilv Cai, Zhijun Zhang, Liqun Chen +3
Learning-based methods have effectively promoted the community of image compression. Meanwhile, variational autoencoder (VAE) based variable-rate approaches have recently gained mu…
Category-Aware Transformer Network for Better Human-Object Interaction Detection
Leizhen Dong, Zhimin Li, Kunlun Xu +4
Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Rec…
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Changfeng Yu, Yi Chang, Yi Li +2
The deep convolutional neural network has achieved significant progress for single image rain streak removal. However, most of the data-driven learning methods are full-supervised…
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Yuntong Ye, Changfeng Yu, Yi Chang +4
Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer.…
Effective Actor-centric Human-object Interaction Detection
Kunlun Xu, Zhimin Li, Zhijun Zhang +5
While Human-Object Interaction(HOI) Detection has achieved tremendous advances in recent, it still remains challenging due to complex interactions with multiple humans and objects…
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Yanjie Wang, Xu Zou, Zhijun Zhang +5
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images. Despite that many methods proposed recently hav…
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Yuntong Ye, Yi Chang, Hanyu Zhou +1
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy…
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Yi Chang, Luxin Yan, Houzhang Fang +2
Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limit…