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 931–940 of 2167 papers

Title	Date	Tasks	Status
Knowledge-Based Visual Question Answering in Videos	Apr 17, 2020	Question AnsweringVideo Question Answering	—Unverified
Knowledge Condensation and Reasoning for Knowledge-based VQA	Mar 15, 2024	Question AnsweringReading Comprehension	—Unverified
COIN: Counterfactual Image Generation for VQA Interpretation	Jan 10, 2022	counterfactualImage Generation	—Unverified
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering	Jun 8, 2023	Question AnsweringRetrieval	—Unverified
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge	May 30, 2023	Answer SelectionQuestion Answering	—Unverified
Assessing the Robustness of Visual Question Answering Models	Nov 30, 2019	Question AnsweringVisual Question Answering	—Unverified
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering	Jan 1, 2021	Novel ConceptsQuestion Answering	—Unverified
Generalized Hadamard-Product Fusion Operators for Visual Question Answering	Mar 26, 2018	Neural Architecture SearchQuestion Answering	—Unverified
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems	Oct 26, 2022	Question AnsweringVisual Question Answering	—Unverified
Assessing Image Quality Issues for Real-World Problems	Mar 27, 2020	Image CaptioningQuestion Answering	—Unverified

Show:10 25 50

← PrevPage 94 of 217Next →

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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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