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 1831–1840 of 2167 papers

Title	Date	Tasks	Status
OmniNet: A unified architecture for multi-modal multi-task learning	Jul 17, 2019	Image CaptioningMulti-Task Learning	CodeCode Available
2nd Place Solution to the GQA Challenge 2019	Jul 16, 2019	Question AnsweringVisual Question Answering	—Unverified
Assessing Visual Quality of Omnidirectional Videos	Jul 14, 2019	Visual Question Answering (VQA)	—Unverified
Neural Reasoning, Fast and Slow, for Video Question Answering	Jul 10, 2019	Natural QuestionsQuestion Answering	—Unverified
Learning by Abstraction: The Neural State Machine	Jul 9, 2019	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available
Are Red Roses Red? Evaluating Consistency of Question-Answering Models	Jul 1, 2019	Question Answeringvalid	CodeCode Available
Multi-grained Attention with Object-level Grounding for Visual Question Answering	Jul 1, 2019	ObjectQuestion Answering	—Unverified
ICDAR 2019 Competition on Scene Text Visual Question Answering	Jun 30, 2019	Question AnsweringVisual Question Answering	—Unverified
Deep Modular Co-Attention Networks for Visual Question Answering	Jun 25, 2019	Question AnsweringVisual Question Answering	CodeCode Available
RUBi: Reducing Unimodal Biases in Visual Question Answering	Jun 24, 2019	Question AnsweringVisual Question Answering	CodeCode Available

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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	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