Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 581–590 of 2167 papers

Title	Date	Tasks	Status	Hype
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research	Mar 17, 2025	ArticlesBenchmarking	CodeCode Available	1
Mimic In-Context Learning for Multimodal Tasks	Apr 11, 2025	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA	Oct 10, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts	May 18, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA	Jan 10, 2024	Medical Visual Question AnsweringMulti-Task Learning	CodeCode Available	1
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering	Sep 19, 2024	HallucinationHallucination Evaluation	CodeCode Available	1
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions	Jun 19, 2021	Question AnsweringVideo Question Answering	CodeCode Available	1
Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models	Oct 9, 2023	Language ModellingQuestion Answering	CodeCode Available	1
MLP Architectures for Vision-and-Language Modeling: An Empirical Study	Dec 8, 2021	Language ModelingLanguage Modelling	CodeCode Available	1
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment	Apr 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified