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 201–225 of 2167 papers

Title	Date	Tasks	Status	Hype
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos	Aug 31, 2022	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
A Dataset and Baselines for Visual Question Answering on Art	Aug 28, 2020	Question AnsweringQuestion Generation	CodeCode Available	1
AMD-Hummingbird: Towards an Efficient Text-to-Video Model	Mar 24, 2025	Computational EfficiencyVideo Generation	CodeCode Available	1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations	Feb 10, 2024	DiagnosticHallucination	CodeCode Available	1
Attention in Reasoning: Dataset, Analysis, and Modeling	Apr 20, 2022	Question AnsweringVisual Question Answering	CodeCode Available	1
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation	Jan 6, 2025	Language Model EvaluationLanguage Modeling	CodeCode Available	1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant	Aug 19, 2024	DescriptiveFace Swapping	CodeCode Available	1
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator	Dec 11, 2023	Image CaptioningQuestion Answering	CodeCode Available	1
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules	May 11, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers	Mar 29, 2021	DecoderImage Segmentation	CodeCode Available	1
Hierarchical Question-Image Co-Attention for Visual Question Answering	May 31, 2016	Visual DialogVisual Question Answering	CodeCode Available	1
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering	Jun 16, 2023	Image CaptioningQuestion Answering	CodeCode Available	1
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts	Nov 16, 2024	Mixture-of-ExpertsOptical Character Recognition (OCR)	CodeCode Available	1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection	Nov 5, 2023	Anomaly DetectionQuestion Answering	CodeCode Available	1
Attention-Based Context Aware Reasoning for Situation Recognition	Jun 1, 2020	Action RecognitionFine-grained Action Recognition	CodeCode Available	1
Graph Optimal Transport for Cross-Domain Alignment	Jun 26, 2020	Graph MatchingImage Captioning	CodeCode Available	1
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning	Oct 3, 2024	Backdoor AttackCross-Modal Retrieval	CodeCode Available	1
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images	Oct 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion	Feb 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
HallE-Control: Controlling Object Hallucination in Large Multimodal Models	Oct 3, 2023	AttributeDecoder	CodeCode Available	1
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1
Can I Trust Your Answer? Visually Grounded Video Question Answering	Sep 4, 2023	Grounded Video Question AnsweringQuestion Answering	CodeCode Available	1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!	Apr 12, 2020	Question AnsweringVisual Grounding	CodeCode Available	1
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
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering	Jul 22, 2023	Graph Representation LearningLanguage Modeling	CodeCode Available	1

Show:10 25 50

← PrevPage 9 of 87Next →

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