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of 289
pro vyhledávání: '"Fu, Xianping"'
Despite significant progress has been made in image deraining, existing approaches are mostly carried out on low-resolution images. The effectiveness of these methods on high-resolution images is still unknown, especially for ultra-high-definition (U
Externí odkaz:
http://arxiv.org/abs/2405.17074
Autor:
Wang, Huibing, Yao, Mingze, Chen, Yawei, Xu, Yunqiu, Liu, Haipeng, Jia, Wei, Fu, Xianping, Wang, Yang
Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incompl
Externí odkaz:
http://arxiv.org/abs/2405.10987
Person search aims to localize specific a target person from a gallery set of images with various scenes. As the scene of moving pedestrian changes, the captured person image inevitably bring in lots of background noise and foreground noise on the pe
Externí odkaz:
http://arxiv.org/abs/2405.02834
Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods de
Externí odkaz:
http://arxiv.org/abs/2405.02832
Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object
Externí odkaz:
http://arxiv.org/abs/2312.06999
In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and
Externí odkaz:
http://arxiv.org/abs/2312.06955
Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploi
Externí odkaz:
http://arxiv.org/abs/2301.02484
Publikováno v:
In Engineering Applications of Artificial Intelligence October 2024 136 Part B
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena June 2024 183
Publikováno v:
In Pattern Recognition June 2024 150