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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 391–400 of 2167 papers

Title	Date	Tasks	Status	Hype
3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models	Sep 28, 2024	DiagnosticLanguage Modeling	—Unverified	0
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations	Sep 27, 2024	Chart Question AnsweringQuestion Answering	—Unverified	0
DARE: Diverse Visual Question Answering with Robustness Evaluation	Sep 26, 2024	image-classificationImage Classification	—Unverified	0
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue	Sep 26, 2024	Medical Visual Question AnsweringQuestion Answering	—Unverified	0
A Unified Hallucination Mitigation Framework for Large Vision-Language Models	Sep 24, 2024	HallucinationQuestion Answering	CodeCode Available	0
MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models	Sep 23, 2024	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
Advancing Video Quality Assessment for AIGC	Sep 23, 2024	Image GenerationText Generation	—Unverified	0
Revisiting Video Quality Assessment from the Perspective of Generalization	Sep 23, 2024	Image Quality AssessmentVideo Quality Assessment	CodeCode Available	0
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation	Sep 23, 2024	Multiple-choiceQuestion Answering	—Unverified	0
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models	Sep 23, 2024	Decision MakingQuestion Answering	CodeCode Available	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