9 citations · 24 across the 6 of their papers we have counts for
6 papers
Rethinking Real-world Image Deraining via An Unpaired Degradation-Conditioned Diffusion Model
Yiyang Shen, Mingqiang Wei, Yongzhen Wang +2
Recent diffusion models have exhibited great potential in generative modeling tasks. Part of their success can be attributed to the ability of training stable on huge sets of paire…
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
Yiyang Shen, Rongwei Yu, Peng Wu +4
LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing method…
PSFormer: Point Transformer for 3D Salient Object Detection
Baian Chen, Lipeng Gu, Xin Zhuang +3
We propose PSFormer, an effective point transformer model for 3D salient object detection. PSFormer is an encoder-decoder network that takes full advantage of transformers to model…
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Yiyang Shen, Yongzhen Wang, Mingqiang Wei +4
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain…
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
Yiyang Shen, Mingqiang Wei, Sen Deng +5
The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such…
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
Yiyang Shen, Yidan Feng, Sen Deng +4
Rain severely hampers the visibility of scene objects when images are captured through glass in heavily rainy days. We observe three intriguing phenomenons that, 1) rain is a mixtu…