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 1011–1020 of 2167 papers

Title	Date	Tasks	Status	Hype
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical Knowledge	Jan 1, 2023	Decision MakingQuestion Answering	CodeCode Available	0
Decouple Before Interact: Multi-Modal Prompt Learning for Continual Visual Question Answering	Jan 1, 2023	Continual LearningLanguage Modelling	—Unverified	0
VQACL: A Novel Visual Question Answering Continual Learning Setting	Jan 1, 2023	Continual LearningQuestion Answering	CodeCode Available	1
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models	Jan 1, 2023	Question AnsweringVisual Question Answering	—Unverified	0
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases	Jan 1, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Dynamic Inference With Grounding Based Vision and Language Models	Jan 1, 2023	Language ModellingReferring Expression	—Unverified	0
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training	Dec 30, 2022	cross-modal alignmentTGIF-Action	—Unverified	0
VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges	Dec 26, 2022	Representation LearningVisual Question Answering (VQA)	—Unverified	0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models	Dec 22, 2022	Attributeimage-classification	—Unverified	0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering	Dec 21, 2022	Data AugmentationDecision Making	—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