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.

🔍 Motivation

PopVQA was curated to explore the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. This is achieved by asking the same questions twice, once with the textual representation (the entity's name), then, with the visual representation (entity image). We include several questions about every entity to allow a more fine grained evaluation. This dataset was introduced in the paper:

"Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models"
Ido Cohen, Daniela Gottesman, Mor Geva, Raja Giryes (2025)

📦 Dataset Structure

The dataset consists of:

• entities.csv: Metadata of 15,395 popular entities, of various types (celebrities, landmarks, logos, and paintings).
• questions.csv: Over 100,000 factual questions, each given in two forms: one referring to a textual representation and one referring to a visual representation of the entity.
• original_path/: Original images.
• resized_path/: Images resized to 336×336 with aspect ratio preserved via padding.

entities.csv columns:

Column	Description
type	Entity type (e.g., celebs, logos)
subject	Entity name
s_uri	Wikidata URI of the subject
popularity	Wikipedia popularity score
aliases	Alternate names/aliases for the entity
image	wiki commons url
original_path	Path to the original image
resized_path	Path to the 336x336 padded image

questions.csv columns:

Column	Description
type	Entity type
subject	Entity name
question_for_image	Question phrased for visual context (e.g., “...in this image?”)
question	Textual version of the same question
possible_answers	List of acceptable answers
relation	Relation name (e.g., occupation, language)
s_uri, r_uri, a_uri	Wikidata URIs for subject, relation, and answer
attribute, a_type	Answer string and attribute types (e.g., "language")