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 291–300 of 2167 papers

Title	Date	Tasks	Status	Hype
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding	Jul 3, 2023	Image-text matchingSentence	CodeCode Available	1
Multimodal Prompt Retrieval for Generative Visual Question Answering	Jun 30, 2023	Domain AdaptationGenerative Visual Question Answering	CodeCode Available	1
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering	Jun 29, 2023	Answer GenerationQuestion Answering	CodeCode Available	1
Kosmos-2: Grounding Multimodal Large Language Models to the World	Jun 26, 2023	Image CaptioningIn-Context Learning	CodeCode Available	1
FunQA: Towards Surprising Video Comprehension	Jun 26, 2023	Question AnsweringText Generation	CodeCode Available	1
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering	Jun 16, 2023	Image CaptioningQuestion Answering	CodeCode Available	1
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model	Jun 15, 2023	Formmodel	CodeCode Available	1
Improving Selective Visual Question Answering by Learning from Your Peers	Jun 14, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1
Scalable Neural-Probabilistic Answer Set Programming	Jun 14, 2023	Probabilistic ProgrammingQuestion Answering	CodeCode Available	1
Modular Visual Question Answering via Code Generation	Jun 8, 2023	Code GenerationIn-Context Learning	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