idoco/PopVQA
PopVQA: Popular Entity Visual Question Answering PopVQA is a dataset designed to study the performance gap in vision-language models (VLMs) when answering factual questions about entities presented in images versus text. Paper: https://huggingface.co/papers/2412.14133 Code: https://github.com/ido-co/vlm-modality-gap 🔍 Motivation PopVQA was curated to explore the disparity in model performance when answering factual questions about an entity described in text… See the full description on the dataset page: https://huggingface.co/datasets/idoco/PopVQA.
| Author / organization | idoco |
|---|---|
| License (as declared) | mit |
| Downloads | 408 (as reported by Hugging Face) |
| Likes | 5 |
| Created | 8 May 2025 |
| Last updated at source | 31 August 2025 |
Popularity, last 2 days
408 now (+1 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.