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 1271–1280 of 2167 papers

Title	Date	Tasks	Status
What BERT Sees: Cross-Modal Transfer for Visual Question Generation	Feb 25, 2020	Question GenerationQuestion-Generation	—Unverified
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering	Dec 13, 2023	Medical Visual Question AnsweringQuestion Answering	—Unverified
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning	Oct 8, 2024	Image RetrievalMath	—Unverified
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis	May 1, 2024	Image CaptioningQuestion Answering	—Unverified
Beyond the Hype: A dispassionate look at vision-language models in medical scenario	Aug 16, 2024	Question AnsweringSpatial Reasoning	—Unverified
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions	Oct 24, 2020	General ClassificationMultiple-choice	—Unverified
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations	Jan 31, 2024	Question AnsweringVisual Question Answering (VQA)	—Unverified
BloomVQA: Assessing Hierarchical Multi-modal Comprehension	Dec 20, 2023	Data AugmentationMemorization	—Unverified
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining	Jan 12, 2024	Question AnsweringVisual Question Answering	—Unverified
BRAVE: Broadening the visual encoding of vision-language models	Apr 10, 2024	HallucinationLanguage Modelling	—Unverified

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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