Consistent Prompt Tuning for Generalized Category Discovery

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Muli Yang, Jie Yin, Yanan Gu, Cheng Deng, Hanwang Zhang, Hongyuan Zhu
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引用次数: 0

Abstract

Generalized Category Discovery (GCD) aims at discovering both known and unknown classes in unlabeled data, using the knowledge learned from a limited set of labeled data. Despite today’s foundation models being trained with Internet-scale multi-modal corpus, we find that they still struggle in GCD due to the ambiguity in class definitions. In this paper, we present Consistent Prompt Tuning (CPT) to disambiguate the classes for large vision-language models (e.g., CLIP). To this end, CPT learns a set of “task + class” prompts for labeled and unlabeled data of both known and unknown classes, with the “task” tokens globally shared across classes, which contain a unified class definition pattern, e.g., “the foreground is an animal named” or “the background scene is”. These prompts are optimized with two efficient regularization techniques that encourage consistent global and local relationships between any two matched inputs. CPT is evaluated on various existing GCD benchmarks, as well as in new practical scenarios with fewer annotations and customized class definitions, demonstrating clear superiority and broad versatility over existing state-of-the-art methods.

通用类别发现的一致提示调优
广义类别发现(GCD)旨在利用从有限的标记数据集中学习到的知识,发现未标记数据中的已知和未知类。尽管今天的基础模型正在使用互联网规模的多模态语料库进行训练,但我们发现由于类定义的模糊性,它们仍然在GCD中挣扎。在本文中,我们提出了一致提示调优(CPT)来消除大型视觉语言模型(例如CLIP)的类歧义。为此,CPT学习了一组“任务+类”提示,用于已知和未知类的标记和未标记数据,“任务”令牌在类之间全局共享,其中包含统一的类定义模式,例如,“前景是一个命名的动物”或“背景场景是”。这些提示使用两种有效的正则化技术进行了优化,这些技术鼓励在任何两个匹配的输入之间建立一致的全局和局部关系。CPT在各种现有的GCD基准测试中进行评估,以及在新的实际场景中使用更少的注释和定制的类定义,显示出与现有的最先进的方法相比,CPT具有明显的优势和广泛的通用性。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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