# TV100: A TV Series Dataset that Pre-Trained CLIP Has Not Seen

arXiv: https://arxiv.org/abs/2404.12407 · HTML: https://mlsanity.com/paper/2404.12407/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [Learning Placeholders for Open-Set Recognition](https://mlsanity.com/paper/2103.15086/) (2021)
- [PILOT: A Pre-Trained Model-Based Continual Learning Toolbox](https://mlsanity.com/paper/2309.07117/) (2023)

## Built on by (most cited first, 2 of 2)

- [C3Box: A CLIP-based Class-Incremental Learning Toolbox](https://mlsanity.com/paper/2601.20852/) (2026)
- [LiteEmbed: Adapting CLIP to Rare Classes](https://mlsanity.com/paper/2601.09661/) (2026)

## Abstract (arXiv)

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 incremental learning, novel class discovery, and long-tailed learning, among others. Project page: https://tv-100.github.io/