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 201–225 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts	Feb 17, 2021	Caption GenerationDiversity	CodeCode Available	1	5
A Dataset and Baselines for Visual Question Answering on Art	Aug 28, 2020	Question AnsweringQuestion Generation	CodeCode Available	1	5
AMD-Hummingbird: Towards an Efficient Text-to-Video Model	Mar 24, 2025	Computational EfficiencyVideo Generation	CodeCode Available	1	5
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model	Jun 15, 2023	Formmodel	CodeCode Available	1	5
Counterfactual Samples Synthesizing for Robust Visual Question Answering	Mar 14, 2020	counterfactualQuestion Answering	CodeCode Available	1	5
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation	Jan 6, 2025	Language Model EvaluationLanguage Modeling	CodeCode Available	1	5
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos	Aug 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1	5
GRIT: General Robust Image Task Benchmark	Apr 28, 2022	Instance SegmentationKeypoint Detection	CodeCode Available	1	5
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models	Jul 26, 2023	Image Quality AssessmentNo-Reference Image Quality Assessment	CodeCode Available	1	5
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions	Dec 8, 2020	counterfactualDescriptive	CodeCode Available	1	5
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling	Nov 23, 2021	Image CaptioningImage Description	CodeCode Available	1	5
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	May 27, 2023	Image CaptioningImage Retrieval	CodeCode Available	1	5
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts	Nov 16, 2024	Mixture-of-ExpertsOptical Character Recognition (OCR)	CodeCode Available	1	5
Attention in Reasoning: Dataset, Analysis, and Modeling	Apr 20, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Cross-Modality Relevance for Reasoning on Language and Vision	May 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Cross-modal Retrieval for Knowledge-based Visual Question Answering	Jan 11, 2024	Cross-Modal RetrievalQuestion Answering	CodeCode Available	1	5
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning	Oct 3, 2024	Backdoor AttackCross-Modal Retrieval	CodeCode Available	1	5
Hierarchical Question-Image Co-Attention for Visual Question Answering	May 31, 2016	Visual DialogVisual Question Answering	CodeCode Available	1	5
ConceptBert: Concept-Aware Representation for Visual Question Answering	Nov 1, 2020	Common Sense ReasoningQuestion Answering	CodeCode Available	1	5
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback	Oct 8, 2024	MathSequential Decision Making	CodeCode Available	1	5
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1	5
HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning	Mar 19, 2024	Reinforcement Learning (RL)Visual Grounding	CodeCode Available	1	5
Greedy Gradient Ensemble for Robust Visual Question Answering	Jul 27, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports	Sep 3, 2020	Image-text RetrievalMedical Visual Question Answering	CodeCode Available	1	5
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models	Mar 23, 2024	Common Sense ReasoningIn-Context Learning	CodeCode Available	1	5

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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