Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Wu, Zhenyao"'
3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation
Externí odkaz:
http://arxiv.org/abs/2303.15654
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 37, 1 (Jun. 2023), 250-258
Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to
Externí odkaz:
http://arxiv.org/abs/2303.08240
Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between the base and novel classes, the state-of-the-art FSS methods may even f
Externí odkaz:
http://arxiv.org/abs/2211.14745
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Exis
Externí odkaz:
http://arxiv.org/abs/2208.09999
Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very challenging problem that benefits many downstream image/video-related task
Externí odkaz:
http://arxiv.org/abs/2207.01600
Autor:
Zhao, Yong, Siriwardane, Edirisuriya M. Dilanga, Wu, Zhenyao, Fu, Nihang, Al-Fahdi, Mohammed, Hu, Ming, Hu, Jianjun
Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuri
Externí odkaz:
http://arxiv.org/abs/2203.14352
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training. In this case, traditional unsupervised domain adaptation model
Externí odkaz:
http://arxiv.org/abs/2112.04665
Vision-based semantic segmentation of waterbodies and nearby related objects provides important information for managing water resources and handling flooding emergency. However, the lack of large-scale labeled training and testing datasets for water
Externí odkaz:
http://arxiv.org/abs/2111.11567
Publikováno v:
In Image and Vision Computing November 2024 151
Publikováno v:
In Computers & Graphics May 2024 120