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 451–460 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Dynamic Language Binding in Relational Visual Reasoning	Apr 30, 2020	ObjectQuestion Answering	CodeCode Available	1	5
Just Ask: Learning to Answer Questions from Millions of Narrated Videos	Dec 1, 2020	Question AnsweringQuestion Generation	CodeCode Available	1	5
KAT: A Knowledge Augmented Transformer for Vision-and-Language	Dec 16, 2021	Answer GenerationDecoder	CodeCode Available	1	5
Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMs	May 29, 2024	Image RetrievalQuestion Answering	CodeCode Available	1	5
3D-Aware Visual Question Answering about Parts, Poses and Occlusions	Oct 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Deep Multimodal Neural Architecture Search	Apr 25, 2020	DecoderImage-text matching	CodeCode Available	1	5
DocFormerv2: Local Features for Document Understanding	Jun 2, 2023	Decoderdocument understanding	CodeCode Available	1	5
Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?	Jun 9, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset	Nov 5, 2024	BenchmarkingLanguage Modeling	CodeCode Available	1	5
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding	Jan 6, 2024	Scene UnderstandingVisual Question Answering (VQA)	CodeCode Available	1	5

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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