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 431–440 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Improving Selective Visual Question Answering by Learning from Your Peers	Jun 14, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Distilled Dual-Encoder Model for Vision-Language Understanding	Dec 16, 2021	Image to textmodel	CodeCode Available	1	5
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering	Jun 29, 2023	Answer GenerationQuestion Answering	CodeCode Available	1	5
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback	Oct 8, 2024	MathSequential Decision Making	CodeCode Available	1	5
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning	Oct 3, 2024	Backdoor AttackCross-Modal Retrieval	CodeCode Available	1	5
Learning to Discretely Compose Reasoning Module Networks for Video Captioning	Jul 17, 2020	DecoderQuestion Answering	CodeCode Available	1	5
In Defense of Grid Features for Visual Question Answering	Jan 10, 2020	Image CaptioningQuestion Answering	CodeCode Available	1	5
Probing Image-Language Transformers for Verb Understanding	Jun 16, 2021	Image RetrievalQuestion Answering	CodeCode Available	1	5
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs	Sep 17, 2024	Question AnsweringToken Reduction	CodeCode Available	1	5
Learning to Answer Visual Questions from Web Videos	May 10, 2022	Dataset GenerationQuestion Answering	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