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 951–975 of 2167 papers

Title	Date	Tasks	Status	Hype
Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering	Mar 22, 2023	Question AnsweringVisual Question Answering	CodeCode Available	0
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering	Mar 21, 2023	4kImage Generation	CodeCode Available	1
eP-ALM: Efficient Perceptual Augmentation of Language Models	Mar 20, 2023	In-Context LearningVisual Question Answering (VQA)	CodeCode Available	1
3D Concept Learning and Reasoning from Multi-View Images	Mar 20, 2023	Question AnsweringVisual Question Answering	—Unverified	0
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering	Mar 19, 2023	Common Sense ReasoningInformation Retrieval	—Unverified	0
VDPVE: VQA Dataset for Perceptual Video Enhancement	Mar 16, 2023	Deblurringvalid	CodeCode Available	1
Logical Implications for Visual Question Answering Consistency	Mar 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	0
GPT-4 Technical Report	Mar 15, 2023	answerability predictionArithmetic Reasoning	CodeCode Available	6
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region	Mar 13, 2023	Question AnsweringVisual Question Answering	—Unverified	0
MRET: Multi-resolution Transformer for Video Quality Assessment	Mar 13, 2023	Video Quality AssessmentVideo Recognition	—Unverified	0
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents	Mar 13, 2023	image-classificationImage Classification	CodeCode Available	2
Vision-Language Models as Success Detectors	Mar 13, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images	Mar 13, 2023	Common Sense ReasoningExplanation Generation	—Unverified	0
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling	Mar 10, 2023	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	—Unverified	0
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models	Mar 10, 2023	Language ModelingLanguage Modelling	CodeCode Available	1
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning	Mar 10, 2023	Few-Shot Image Classificationimage-classification	—Unverified	0
Toward Unsupervised Realistic Visual Question Answering	Mar 9, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Interpretable Visual Question Answering Referring to Outside Knowledge	Mar 8, 2023	DiversityImage Captioning	—Unverified	0
Graph Neural Networks in Vision-Language Image Understanding: A Survey	Mar 7, 2023	Image CaptioningImage Retrieval	—Unverified	0
PaLM-E: An Embodied Multimodal Language Model	Mar 6, 2023	Language ModelingLanguage Modelling	CodeCode Available	2
Knowledge-Based Counterfactual Queries for Visual Question Answering	Mar 5, 2023	counterfactualDecision Making	—Unverified	0
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning	Mar 5, 2023	Answer GenerationEntity Alignment	CodeCode Available	0
Prismer: A Vision-Language Model with Multi-Task Experts	Mar 4, 2023	Few-Shot LearningImage Captioning	CodeCode Available	1
Audio-Visual Quality Assessment for User Generated Content: Database and Method	Mar 4, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering	Mar 3, 2023	Language ModellingLarge Language Model	CodeCode Available	2

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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