no code implementations • 24 Apr 2024 • Guosheng Lu, Zile Fang, Chunming He, Zhigang Zhao
Consequently, fusion outcomes frequently entail a compromise between thermal target area information and texture details.
Generative Adversarial Network Infrared And Visible Image Fusion
1 code implementation • 22 Apr 2024 • Chenyang Zhu, Kai Li, Yue Ma, Chunming He, Li Xiu
MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase.
no code implementations • 22 Jan 2024 • Fengyang Xiao, Pan Zhang, Chunming He, Runze Hu, Yutao Liu
Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments.
1 code implementation • 20 Nov 2023 • Chunming He, Chengyu Fang, Yulun Zhang, Tian Ye, Kai Li, Longxiang Tang, Zhenhua Guo, Xiu Li, Sina Farsiu
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
1 code implementation • 6 Aug 2023 • Chunming He, Kai Li, Yachao Zhang, Yulun Zhang, Zhenhua Guo, Xiu Li, Martin Danelljan, Fisher Yu
On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect.
no code implementations • 3 Aug 2023 • Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, Xiu Li
In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets.
no code implementations • 15 Jul 2023 • Chunming He, Kai Li, Guoxia Xu, Jiangpeng Yan, Longxiang Tang, Yulun Zhang, Xiu Li, YaoWei Wang
Specifically, we extract features from an HQ image and explicitly insert the features, which are expected to encode HQ cues, into the enhancement network to guide the LQ enhancement with the variational normalization module.
1 code implementation • 14 Jul 2023 • Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, Xiu Li
This paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model.
no code implementations • NeurIPS 2023 • Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, Xiu Li
It remains a challenging task since (1) it is hard to distinguish concealed objects from the background due to the intrinsic similarity and (2) the sparsely-annotated training data only provide weak supervision for model learning.
no code implementations • 21 Apr 2023 • mengqun Jin, Kai Li, Shuyan Li, Chunming He, Xiu Li
We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples.
Semi-supervised Domain Adaptation Unsupervised Domain Adaptation
no code implementations • CVPR 2023 • Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, Xiu Li
COD is a challenging task due to the intrinsic similarity of camouflaged objects with the background, as well as their ambiguous boundaries.
no code implementations • ICCV 2023 • Chunming He, Kai Li, Guoxia Xu, Yulun Zhang, Runze Hu, Zhenhua Guo, Xiu Li
Heterogeneous image fusion (HIF) techniques aim to enhance image quality by merging complementary information from images captured by different sensors.