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 521–530 of 2167 papers

Title	Date	Tasks	Status	Hype
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering	Jun 3, 2024	DiversityQuestion Answering	—Unverified	0
Selectively Answering Visual Questions	Jun 3, 2024	AvgIn-Context Learning	—Unverified	0
Ovis: Structural Embedding Alignment for Multimodal Large Language Model	May 31, 2024	Language ModelingMultimodal Large Language Model	CodeCode Available	5
DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models	May 31, 2024	cross-modal alignmentVisual Localization	CodeCode Available	2
VQA Training Sets are Self-play Environments for Generating Few-shot Pools	May 30, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA	May 30, 2024	DiagnosticMedical Diagnosis	CodeCode Available	1
Instruction-Guided Visual Masking	May 30, 2024	Instruction FollowingVisual Grounding	CodeCode Available	1
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks	May 29, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMs	May 29, 2024	Image RetrievalQuestion Answering	CodeCode Available	1
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild	May 28, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—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	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	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