# A Survey of Current Datasets for Vision and Language Research

arXiv: https://arxiv.org/abs/1506.06833 · HTML: https://mlsanity.com/paper/1506.06833/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2015.

## Built on by (most cited first, 13 of 13)

- [MultiBench: Multiscale Benchmarks for Multimodal Representation Learning](https://mlsanity.com/paper/2107.07502/) (2021)
- [Gender Artifacts in Visual Datasets](https://mlsanity.com/paper/2206.09191/) (2022)
- [SOLO: A Single Transformer for Scalable Vision-Language Modeling](https://mlsanity.com/paper/2407.06438/) (2024)
- [HEMM: Holistic Evaluation of Multimodal Foundation Models](https://mlsanity.com/paper/2407.03418/) (2024)
- [Measuring Data](https://mlsanity.com/paper/2212.05129/) (2022)
- [SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark](https://mlsanity.com/paper/2110.10661/) (2021)
- [Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology](https://mlsanity.com/paper/2310.04529/) (2023)
- [VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language Models](https://mlsanity.com/paper/2209.06103/) (2022)
- [Location-Aware Visual Question Generation with Lightweight Models](https://mlsanity.com/paper/2310.15129/) (2023)
- [Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks](https://mlsanity.com/paper/2208.10758/) (2022)
- [Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis](https://mlsanity.com/paper/2412.00056/) (2024)
- [Multi-VQG: Generating Engaging Questions for Multiple Images](https://mlsanity.com/paper/2211.07441/) (2022)
- [MINT: Multimodal Instruction Tuning with Multimodal Interaction Grouping](https://mlsanity.com/paper/2506.02308/) (2025)

## Abstract (arXiv)

Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The available corpora have played a crucial role in advancing this area of research. In this paper, we propose a set of quality metrics for evaluating and analyzing the vision & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been using more complex language and more abstract concepts, however, there are different strengths and weaknesses in each.