TV100: a TV series dataset that pre-trained CLIP has not seen

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2024-06-06 DOI:10.1007/s11704-024-40217-z

Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan

引用次数: 0

Abstract

The era of pre-trained models has ushered in a wealth of new insights for the machine learning community. Among the myriad of questions that arise, one of paramount importance is: ‘Do pre-trained models possess comprehensive knowledge?’ This paper seeks to address this crucial inquiry. In line with our objective, we have made publicly available a novel dataset comprised of images from TV series released post-2021. This dataset holds significant potential for use in various research areas, including the evaluation of novel class iscovery and long-tailed learning, among others.

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TV100：预训练 CLIP 未见过的电视剧数据集

预训练模型时代为机器学习界带来了大量新见解。在出现的无数问题中，最重要的一个问题是："预训练模型是否拥有全面的知识？本文试图解决这一关键问题。根据我们的目标，我们公开了一个由 2021 年后发布的电视剧图像组成的新数据集。该数据集在多个研究领域都具有巨大的应用潜力，其中包括对新类别识别和长尾学习等的评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.