Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2024, Vol. 18 Issue (5) : 185349    https://doi.org/10.1007/s11704-024-40217-z
Artificial Intelligence
TV100: a TV series dataset that pre-trained CLIP has not seen
Da-Wei ZHOU1,2, Zhi-Hong QI1,2, Han-Jia YE1,2(), De-Chuan ZHAN1,2
1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
2. School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
 Download: PDF(1054 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Corresponding Author(s): Han-Jia YE   
Just Accepted Date: 08 May 2024   Issue Date: 31 May 2024
 Cite this article:   
Da-Wei ZHOU,Zhi-Hong QI,Han-Jia YE, et al. TV100: a TV series dataset that pre-trained CLIP has not seen[J]. Front. Comput. Sci., 2024, 18(5): 185349.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-40217-z
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I5/185349
Fig.1  Detailed information about TV100, including the data collection process, the country distribution, and class distribution. It also contains an empirical evaluation of zero-shot and finetuned performance. (a) Data collection pipeline; (b) country distribution; (c) instance distribution; (d) performance evaluation
1 L, Floridi M Chiriatti . GPT-3: its nature, scope, limits, and consequences. Minds and Machines, 2020, 30(4): 681−694
2 A, Ramesh M, Pavlov G, Goh S, Gray C, Voss A, Radford M, Chen I Sutskever . Zero-shot text-to-image generation. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8821−8831
3 A, Radford J W, Kim C, Hallacy A, Ramesh G, Goh S, Agarwal G, Sastry A, Askell P, Mishkin J, Clark G, Krueger I Sutskever . Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8748−8763
4 J, Deng W, Dong R, Socher L J, Li K, Li L Fei-Fei . ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009, 248−255
5 D W, Zhou Q W, Wang Z H, Qi H J, Ye D C, Zhan Z W Liu . Deep class-incremental learning: a survey. 2023, arXiv preprint arXiv: 2302.03648
6 C, Schuhmann R, Beaumont R, Vencu C, Gordon R, Wightman M, Cherti T, Coombes A, Katta C, Mullis M, Wortsman P, Schramowski S, Kundurthy K, Crowson L, Schmidt R, Kaczmarczyk J Jitsev . LAION-5B: an open large-scale dataset for training next generation image-text models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 25278−25294
7 D W, Zhou H J, Ye D C Zhan . Learning placeholders for open-set recognition. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 4401−4410
8 D W, Zhou H L, Sun J Y, Ning H J, Ye D C Zhan . Continual learning with pre-trained models: a survey. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence. 2024
9 H L, Sun D W, Zhou H J, Ye D C Zhan . PILOT: a pre-trained model-based continual learning toolbox. 2023, arXiv preprint arXiv: 2309.07117
10 V, Rawte A, Sheth A Das . A survey of hallucination in large foundation models. 2023, arXiv preprint arXiv: 2309.05922
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed