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...
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...
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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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...
aerial image classificationgeneralized category discoverycontrastive learningslot attentionSCENE CLASSIFICATIONAerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies ...
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...
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} }...