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 71–80 of 2167 papers

Title	Date	Tasks	Status	Hype
Let Androids Dream of Electric Sheep: A Human-like Image Implication Understanding and Reasoning Framework	May 22, 2025	Multiple-choiceVisual Question Answering (VQA)	CodeCode Available	1
Grounding Chest X-Ray Visual Question Answering with Generated Radiology Reports	May 22, 2025	Answer GenerationQuestion Answering	—Unverified	0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment	May 21, 2025	Language ModelingLanguage Modelling	—Unverified	0
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving	May 21, 2025	Autonomous DrivingQuestion Answering	—Unverified	0
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning	May 21, 2025	AllVisual Question Answering (VQA)	—Unverified	0
Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets	May 21, 2025	Dataset GenerationDescriptive	—Unverified	0
SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks	May 21, 2025	image-classificationImage Classification	CodeCode Available	0
Visual Question Answering on Multiple Remote Sensing Image Modalities	May 21, 2025	Question AnsweringVisual Question Answering	—Unverified	0
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models	May 20, 2025	Visual Question Answering (VQA)	—Unverified	0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models	May 20, 2025	Medical Visual Question AnsweringQuestion Answering	—Unverified	0

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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