Zobrazeno 1 - 10
of 136
pro vyhledávání: '"He, Chunming"'
Autor:
Xiao, Fengyang, Hu, Sujie, Shen, Yuqi, Fang, Chengyu, Huang, Jinfa, He, Chunming, Tang, Longxiang, Yang, Ziyun, Li, Xiu
Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems. In recent years, COD has garnered widespread attent
Externí odkaz:
http://arxiv.org/abs/2408.14562
Autor:
Tang, Longxiang, Tian, Zhuotao, Li, Kai, He, Chunming, Zhou, Hantao, Zhao, Hengshuang, Li, Xiu, Jia, Jiaya
This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes vary across tasks. To handle these diverse tasks, pre-trained Vision-Lan
Externí odkaz:
http://arxiv.org/abs/2407.05342
Autor:
He, Chunming, Shen, Yuqi, Fang, Chengyu, Xiao, Fengyang, Tang, Longxiang, Zhang, Yulun, Zuo, Wangmeng, Guo, Zhenhua, Li, Xiu
Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterized by a forward diffusion process and a reverse denoising process, have
Externí odkaz:
http://arxiv.org/abs/2406.11138
Autor:
Fang, Chengyu, He, Chunming, Xiao, Fengyang, Zhang, Yulun, Tang, Longxiang, Zhang, Yuelin, Li, Kai, Li, Xiu
Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To add
Externí odkaz:
http://arxiv.org/abs/2406.07966
This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized generation methods, particularly with the success of diffusion models,
Externí odkaz:
http://arxiv.org/abs/2404.14239
Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments. Despite achieving remarkable success, existing COS segmenters sti
Externí odkaz:
http://arxiv.org/abs/2401.11767
Autor:
He, Chunming, Fang, Chengyu, Zhang, Yulun, Ye, Tian, Li, Kai, Tang, Longxiang, Guo, Zhenhua, Li, Xiu, Farsiu, Sina
Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promisi
Externí odkaz:
http://arxiv.org/abs/2311.11638
Autor:
He, Chunming, Li, Kai, Zhang, Yachao, Zhang, Yulun, Guo, Zhenhua, Li, Xiu, Danelljan, Martin, Yu, Fisher
Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging
Externí odkaz:
http://arxiv.org/abs/2308.03166
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their ada
Externí odkaz:
http://arxiv.org/abs/2308.01587
Autor:
He, Chunming, Li, Kai, Xu, Guoxia, Yan, Jiangpeng, Tang, Longxiang, Zhang, Yulun, Li, Xiu, Wang, Yaowei
Unpaired Medical Image Enhancement (UMIE) aims to transform a low-quality (LQ) medical image into a high-quality (HQ) one without relying on paired images for training. While most existing approaches are based on Pix2Pix/CycleGAN and are effective to
Externí odkaz:
http://arxiv.org/abs/2307.07829