Generalized Category Discovery 下载积分: 199 内容提示: 文档格式:PDF | 页数:14 | 浏览次数:1 | 上传日期:2024-11-08 18:50:22 | 文档星级: 阅读了该文档的用户还阅读了这些文档 13 p. T2Vid: Translating Long Text into Multi-Image is the Catalyst for Video-LLMs 18 p. AlphaTablets: ...
论文地址:[2211.11727] Parametric Classification for Generalized Category Discovery: A Baseline Study (arxiv.org) 代码地址:CVMI-Lab/SimGCD: (ICCV 2023) Parametric Classification for Generalized Cat…
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery Abstract 在本文中,我们解决了广义类别发现(generalized category discovery, GCD)的问题,即给定一组图像,其中一部分被标记,其余部分未标记,任务是利用来自有标签数据的信息,在无标签数据中自动聚类图像,而无标签数据包含来自标记类的...
We address the more unconstrained setting, naming it 'Generalized Category Discovery', and challenge all these assumptions. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. Next, we propose the use of vision ...
论文链接:[2304.14310] Incremental Generalized Category Discovery (arxiv.org) 代码链接:DTennant/Incremental-Generalized-Category-Discovery (github.com) 摘要 我们探讨了增量通用类别发现(IGCD)问题。这是一个极具挑战性的增量分类学习问题,其目标是开发出能够正确分类以前所见类别图像的模型,同时发现新的类别。学习...
Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets, providing a more realistic setting for image recognition. Essentially, GCD needs to remember existing patterns thoroughly to recognize novel categories. Recent state-of-the-art method...
Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on...
One major conundrum with both Generalized Category Discovery and Novel Category Discovery is that the definition of the category has remained undetermined. This complication can be overlooked when the granularity of categories at test time is similar to training time. However, for more realistic applica...
@inproceedings{choi2024contrastive, title={Contrastive Mean-Shift Learning for Generalized Category Discovery}, author={Choi, Sua and Kang, Dahyun and Cho, Minsu}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, year={2024} }...
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