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 1641–1650 of 2167 papers

Title	Date	Tasks	Status
Understanding the Role of Scene Graphs in Visual Question Answering	Jan 14, 2021	Graph GenerationQuestion Answering	—Unverified
Predicting Relative Depth between Objects from Semantic Features	Jan 12, 2021	Question AnsweringVisual Question Answering	—Unverified
Self Supervision for Attention Networks	Jan 6, 2021	image-classificationImage Classification	CodeCode Available
Transformers in Vision: A Survey	Jan 4, 2021	Action RecognitionActivity Recognition	—Unverified
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA	Jan 1, 2021	Question AnsweringVisual Question Answering	—Unverified
Hierarchical Graph Attention Network for Few-Shot Visual-Semantic Learning	Jan 1, 2021	Graph AttentionImage Captioning	—Unverified
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning	Jan 1, 2021	Question AnsweringSelf-Supervised Learning	—Unverified
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering	Jan 1, 2021	Novel ConceptsQuestion Answering	—Unverified
Unshuffling Data for Improved Generalization in Visual Question Answering	Jan 1, 2021	Out-of-Distribution GeneralizationQuestion Answering	—Unverified
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings	Dec 31, 2020	Common Sense ReasoningKnowledge Graph Embeddings	—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