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 241–250 of 2167 papers

Title	Date	Tasks	Status	Hype
Cross-modal Retrieval for Knowledge-based Visual Question Answering	Jan 11, 2024	Cross-Modal RetrievalQuestion 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
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding	Jan 6, 2024	Scene UnderstandingVisual Question Answering (VQA)	CodeCode Available	1
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training	Jan 4, 2024	DescriptiveImage Captioning	CodeCode Available	1
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA	Dec 21, 2023	Contrastive Learningcounterfactual	CodeCode Available	1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks	Dec 21, 2023	Image RetrievalImage-to-Text Retrieval	CodeCode Available	1
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering	Dec 19, 2023	ObjectObject Counting	CodeCode Available	1
HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles	Dec 18, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1
ViLA: Efficient Video-Language Alignment for Video Question Answering	Dec 13, 2023	cross-modal alignmentLanguage Modeling	CodeCode Available	1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator	Dec 11, 2023	Image CaptioningQuestion Answering	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	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