CORE-MM

Dataset Information
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Overview

CORE-MM is an Open-ended VQA benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks. CORE-MM benchmark consists of 279 manually curated reasoning questions, associated with a total of 342 images. The questions are divided into 3 reasoning categories--Deductive, Abductive and Analogical. 49 questions pertain to abductive reasoning, 181 require deductive reasoning, and 49 involve analogicalreasoning. Furthermore, the dataset is divided into two folds based on reasoning complexity, with 108 classified as “High” reasoning complexity and 171 as “Moderate” reasoning complexity.

Variants: CORE-MM

Associated Benchmarks

This dataset is used in 1 benchmark:

Recent Benchmark Submissions

Task Model Paper Date
Visual Question Answering (VQA) GPT-4V GPT-4 Technical Report 2023-03-15

Research Papers

Recent papers with results on this dataset: