Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Zhong, Zhun"'
Publikováno v:
(2024 Conference on Computer Vision and Pattern Recognition)
We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation, which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging proble
Externí odkaz:
http://arxiv.org/abs/2406.06813
Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distribution (ID) data, has been possible with the advent of vision-language
Externí odkaz:
http://arxiv.org/abs/2406.00806
Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data locally. In practice, distinct model architectures, varying data distributi
Externí odkaz:
http://arxiv.org/abs/2405.17267
Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cost makes it resource-consuming in capturing long-range visual-language
Externí odkaz:
http://arxiv.org/abs/2403.17839
Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while larg
Externí odkaz:
http://arxiv.org/abs/2403.13225
In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labeled data from known categories. Current GCD methods rely on only visua
Externí odkaz:
http://arxiv.org/abs/2403.07369
Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates sha
Externí odkaz:
http://arxiv.org/abs/2403.07347
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old clas
Externí odkaz:
http://arxiv.org/abs/2403.04272
Generalized category discovery (GCD) aims at addressing a more realistic and challenging setting of semi-supervised learning, where only part of the category labels are assigned to certain training samples. Previous methods generally employ naive con
Externí odkaz:
http://arxiv.org/abs/2401.13325
Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR). It has tremendous significance in real-world applications since an average layperson does not
Externí odkaz:
http://arxiv.org/abs/2401.13837