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 251–260 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering	Jul 13, 2021	NavigateQuestion Answering	CodeCode Available	1	5
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	CodeCode Available	1	5
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	May 27, 2023	Image CaptioningImage Retrieval	CodeCode Available	1	5
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering	Apr 20, 2021	Graph Neural NetworkGraph Question Answering	CodeCode Available	1	5
Graph Optimal Transport for Cross-Domain Alignment	Jun 26, 2020	Graph MatchingImage Captioning	CodeCode Available	1	5
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions	Dec 8, 2020	counterfactualDescriptive	CodeCode Available	1	5
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Apr 26, 2023	DecoderKnowledge Distillation	CodeCode Available	1	5
Counterfactual VQA: A Cause-Effect Look at Language Bias	Jun 8, 2020	Causal Inferencecounterfactual	CodeCode Available	1	5
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering	Jul 26, 2022	Causal InferenceQuestion Answering	CodeCode Available	1	5
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering	Feb 25, 2019	Question AnsweringVisual 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