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 1521–1530 of 2167 papers

Title	Date	Tasks	Status
Perceptual Score: What Data Modalities Does Your Model Perceive?	Oct 27, 2021	Question AnsweringVisual Dialog	CodeCode Available
Subtleties in the trainability of quantum machine learning models	Oct 27, 2021	BIG-bench Machine LearningQuantum Machine Learning	—Unverified
Alignment Attention by Matching Key and Query Distributions	Oct 25, 2021	Graph AttentionQuestion Answering	CodeCode Available
Robustness through Data Augmentation Loss Consistency	Oct 21, 2021	Multi-domain Dialogue State TrackingVisual Question Answering	CodeCode Available
Single-Modal Entropy based Active Learning for Visual Question Answering	Oct 21, 2021	Active LearningQuestion Answering	—Unverified
Evaluating and Improving Interactions with Hazy Oracles	Oct 19, 2021	Object TrackingReferring Expression	—Unverified
Towards Language-guided Visual Recognition via Dynamic Convolutions	Oct 17, 2021	Question AnsweringReferring Expression	CodeCode Available
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation	Oct 16, 2021	Common Sense ReasoningEmbodied Question Answering	—Unverified
xGQA: Cross-Lingual Visual Question Answering	Oct 16, 2021	Cross-Lingual TransferLanguage Modeling	—Unverified
Guiding Visual Question Generation	Oct 15, 2021	Question GenerationQuestion-Generation	—Unverified

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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