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 576–600 of 2167 papers

Title	Date	Tasks	Status	Hype
BRAVE: Broadening the visual encoding of vision-language models	Apr 10, 2024	HallucinationLanguage Modelling	—Unverified	0
OmniFusion Technical Report	Apr 9, 2024	MM-VetTextVQA	CodeCode Available	0
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding	Apr 8, 2024	GPUMultiple-choice	CodeCode Available	3
HAMMR: HierArchical MultiModal React agents for generic VQA	Apr 8, 2024	Optical Character Recognition (OCR)Question Answering	—Unverified	0
Study of the effect of Sharpness on Blind Video Quality Assessment	Apr 6, 2024	SSIMVideo Quality Assessment	—Unverified	0
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models	Apr 6, 2024	MMEObject	CodeCode Available	0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction	Apr 5, 2024	Document Classificationdocument understanding	—Unverified	0
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices	Apr 4, 2024	QuantizationQuestion Answering	—Unverified	0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs	Apr 1, 2024	Common Sense ReasoningObject	—Unverified	0
Evaluating Text-to-Visual Generation with Image-to-Text Generation	Apr 1, 2024	Image to textQuestion Answering	CodeCode Available	3
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training	Mar 30, 2024	Contrastive LearningQuestion Answering	CodeCode Available	0
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models	Mar 29, 2024	Question AnsweringVisual Question Answering	CodeCode Available	2
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models	Mar 28, 2024	HallucinationQuestion Answering	CodeCode Available	1
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective	Mar 27, 2024	Question AnsweringVisual Question Answering	CodeCode Available	1
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations	Mar 26, 2024	HallucinationImage Captioning	—Unverified	0
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions	Mar 26, 2024	Gaze Target EstimationQuestion Answering	—Unverified	0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering	Mar 26, 2024	Decision MakingExplainable artificial intelligence	CodeCode Available	0
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning	Mar 25, 2024	Visual Question Answering (VQA)	CodeCode Available	3
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA	Mar 25, 2024	Chart Question AnsweringData Augmentation	—Unverified	0
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models	Mar 23, 2024	Common Sense ReasoningIn-Context Learning	CodeCode Available	1
Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery	Mar 22, 2024	Language ModelingLanguage Modelling	—Unverified	0
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis	Mar 22, 2024	Medical DiagnosisMedical Visual Question Answering	CodeCode Available	2
Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering	Mar 21, 2024	object-detectionObject Detection	CodeCode Available	1
Multi-Modal Hallucination Control by Visual Information Grounding	Mar 20, 2024	HallucinationVisual Question Answering (VQA)	—Unverified	0
vid-TLDR: Training Free Token merging for Light-weight Video Transformer	Mar 20, 2024	Action RecognitionComputational Efficiency	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