给不同的class 产生单独对应的cluster就更完美了,后者相对于前者的简单的分类任务更困难,但最终得到的模型也更加鲁棒,因为当模型不仅能正确区分不同类别,并且可以给每个class产生独立的cluster(即不仅增大了类间距离,同时也减少了类内距离,从而使得整个representation spaces非常的紧凑而分明),那么这样的决策平面会更加rob...
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 ...
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To address these challenges, we propose a novel computational framework, named AnnoGCD, building on Generalized Category Discovery (GCD) and Anomaly Detection (AD) for automatic cell type annotation. Our semi-supervised method combines labeled and unlabeled data to accurately classify known cell types...
代码地址:CVMI-Lab/SimGCD: (ICCV 2023) Parametric Classification for Generalized Category Discovery: A Baseline Study (github.com) 摘要 广义类别发现(GCD)旨在利用从有标签样本中学到的知识,在无标签数据集中发现新类别。以前的研究认为,参数分类器容易过度拟合所见类别,并赞同使用半监督 k 均值形成的非参数分...
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...
While strides have been made in the realms of self-supervised and open-world learning towards test-time category discovery, a crucial yet often overlooked question persists: what exactly delineates a category? In this paper, we conceptualize a category through the lens of optimization, viewing it...
@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} }...