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 1041–1050 of 2167 papers

Title	Date	Tasks	Status	Hype
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals	Nov 29, 2022	Deep LearningQuestion Answering	—Unverified	0
Neuro-Symbolic Spatio-Temporal Reasoning	Nov 28, 2022	AI AgentImage Segmentation	—Unverified	0
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning	Nov 24, 2022	cross-modal alignmentImage-text Retrieval	CodeCode Available	1
Self-supervised vision-language pretraining for Medical visual question answering	Nov 24, 2022	Contrastive LearningImage-text matching	CodeCode Available	1
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images	Nov 23, 2022	Optical Character Recognition (OCR)Question Answering	—Unverified	0
A Short Survey of Systematic Generalization	Nov 22, 2022	SurveySystematic Generalization	—Unverified	0
X^2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks	Nov 22, 2022	AllCross-Modal Retrieval	CodeCode Available	2
Cross-Modal Contrastive Learning for Robust Reasoning in VQA	Nov 21, 2022	Contrastive LearningQuestion Answering	CodeCode Available	0
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations	Nov 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference	Nov 21, 2022	Natural Language InferenceQuestion Answering	—Unverified	0

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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