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 1001–1010 of 2167 papers

Title	Date	Tasks	Status	Hype
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images	Jan 23, 2023	AttributeQuestion Answering	—Unverified	0
Champion Solution for the WSDM2023 Toloka VQA Challenge	Jan 22, 2023	Question AnsweringVisual Grounding	CodeCode Available	3
Towards Models that Can See and Read	Jan 18, 2023	DecoderImage Captioning	—Unverified	0
Curriculum Script Distillation for Multilingual Visual Question Answering	Jan 17, 2023	Question AnsweringVisual Question Answering	—Unverified	0
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images	Jan 12, 2023	Evidence SelectionQuestion Answering	CodeCode Available	1
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks	Jan 12, 2023	Cross-Modal RetrievalOpen-Ended Question Answering	CodeCode Available	0
Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering	Jan 11, 2023	Question AnsweringReading Comprehension	CodeCode Available	1
Adaptively Clustering Neighbor Elements for Image-Text Generation	Jan 5, 2023	ClusteringDecoder	CodeCode Available	0
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3	Jan 1, 2023	Image CaptioningQuestion Answering	—Unverified	0
Variational Causal Inference Network for Explanatory Visual Question Answering	Jan 1, 2023	Explanation GenerationExplanatory Visual Question 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