TV100: a TV series dataset that pre-trained CLIP has not seen
Conclusion
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.
References
[1]
Floridi L and Chiriatti M GPT-3: its nature, scope, limits, and consequences Minds and Machines 2020 30 4 681-694
[2]
Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I. Zero-shot text-to-image generation. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8821–8831
[3]
Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 8748–8763
[4]
Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. 2009, 248–255
[5]
Zhou D W, Wang Q W, Qi Z H, Ye H J, Zhan D C, Liu Z W. Deep class-incremental learning: a survey. 2023, arXiv preprint arXiv: 2302.03648
[6]
Schuhmann C, Beaumont R, Vencu R, Gordon C, Wightman R, Cherti M, Coombes T, Katta A, Mullis C, Wortsman M, Schramowski P, Kundurthy S, Crowson K, Schmidt L, Kaczmarczyk R, Jitsev J. 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]
Zhou D W, Ye H J, Zhan D C. Learning placeholders for open-set recognition. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 4401–4410
[8]
Zhou D W, Sun H L, Ning J Y, Ye H J, Zhan D C. Continual learning with pre-trained models: a survey. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence. 2024
[9]
Sun H L, Zhou D W, Ye H J, Zhan D C. PILOT: a pre-trained modelbased continual learning toolbox. 2023, arXiv preprint arXiv: 2309.07117
[10]
Rawte V, Sheth A, Das A. A survey of hallucination in large foundation models. 2023, arXiv preprint arXiv: 2309.05922
Recommendations
wCM based hybrid pre-processing algorithm for class imbalanced dataset
Imbalanced dataset classification is challenging because of the severely skewed class distribution. The traditional machine learning algorithms show degraded performance for these skewed datasets. However, there are additional characteristics of a ...
Effectiveness of convolutional layers in pre-trained models for classifying common weeds in groundnut and corn crops
Highlights- A new balanced and multi-class dataset for groundnut crops with 15 common weeds has been taken from different locations in Andrapradesh and Tamilnadu states ...
AbstractIn modern agriculture, herbicides are most commonly used to control weeds. A large amount of herbicide usage not only has an adverse effect on the soil but also has a severe environmental impact. A smart herbicide sprayer combines the ...
Graphical abstractDisplay Omitted
Fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening: a clinical study
Diabetic retinopathy is a serious complication of diabetes, and if not controlled, may cause blindness. Automated screening of diabetic retinopathy helps physicians to diagnose and control the disease in early stages. In this paper, two case studies are ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© Higher Education Press 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 06 June 2024
Accepted: 05 May 2024
Received: 01 March 2024
Qualifiers
- Letter
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Dec 2024