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 1681–1690 of 2167 papers

Title	Date	Tasks	Status
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View	Oct 30, 2020	Face Recognitionimage-classification	CodeCode Available
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis	Oct 28, 2020	Auxiliary LearningImage Generation	—Unverified
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions	Oct 24, 2020	General ClassificationMultiple-choice	—Unverified
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency	Oct 20, 2020	Question AnsweringVisual Grounding	CodeCode Available
Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering	Oct 17, 2020	Question AnsweringVisual Question Answering	—Unverified
New Ideas and Trends in Deep Multimodal Content Understanding: A Review	Oct 16, 2020	Cross-Modal RetrievalDeep Learning	—Unverified
Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!	Oct 13, 2020	DiagnosticImage-text Classification	—Unverified
Interpretable Neural Computation for Real-World Compositional Visual Question Answering	Oct 10, 2020	Question AnsweringVisual Question Answering	—Unverified
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset	Oct 8, 2020	Question AnsweringVisual Question Answering	—Unverified
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering	Oct 6, 2020	Optical Character RecognitionOptical Character Recognition (OCR)	—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