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 1741–1750 of 2167 papers

Title	Date	Tasks	Status	Hype
SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions	Jan 20, 2020	Visual Question Answering (VQA)	—Unverified	0
Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models	Jan 20, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations	Jan 20, 2020	Question AnsweringRecommendation Systems	CodeCode Available	0
Extending Class Activation Mapping Using Gaussian Receptive Field	Jan 15, 2020	Deep LearningImage Classification	—Unverified	0
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features	Jan 14, 2020	ClassificationDiversity	CodeCode Available	1
MHSAN: Multi-Head Self-Attention Network for Visual Semantic Embedding	Jan 11, 2020	Image CaptioningImage-text Retrieval	CodeCode Available	0
In Defense of Grid Features for Visual Question Answering	Jan 10, 2020	Image CaptioningQuestion Answering	CodeCode Available	1
Visual Question Answering on 360° Images	Jan 10, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Think Locally, Act Globally: Federated Learning with Local and Global Representations	Jan 6, 2020	Federated LearningRepresentation Learning	CodeCode Available	1
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering	Jan 3, 2020	Question AnsweringVideo Description	—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