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 901–925 of 2167 papers

Title	Date	Tasks	Status	Hype
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner	May 19, 2023	Dense CaptioningImage Captioning	CodeCode Available	1
Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery	May 19, 2023	Answer Generationobject-detection	CodeCode Available	1
MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts	May 18, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities	May 18, 2023	1 Image, 2*2 StitchiAction Classification	CodeCode Available	3
Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature	May 18, 2023	Question AnsweringVisual Question Answering	—Unverified	0
An Empirical Study on the Language Modal in Visual Question Answering	May 17, 2023	Question AnsweringVisual Question Answering	—Unverified	0
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering	May 17, 2023	BenchmarkingDiagnostic	CodeCode Available	1
TG-VQA: Ternary Game of Video Question Answering	May 17, 2023	Contrastive LearningQuestion Answering	—Unverified	0
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning	May 17, 2023	Quantum Machine LearningVisual Question Answering (VQA)	—Unverified	0
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement	May 16, 2023	Video EnhancementVideo Quality Assessment	CodeCode Available	1
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement	May 15, 2023	Video EnhancementVideo Quality Assessment	—Unverified	0
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models	May 13, 2023	Key Information ExtractionNutrition	CodeCode Available	2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning	May 11, 2023	1 Image, 2*2 StitchingDiversity	CodeCode Available	2
Combo of Thinking and Observing for Outside-Knowledge VQA	May 10, 2023	DecoderQuestion Answering	CodeCode Available	1
OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese	May 7, 2023	Information RetrievalQuestion Answering	CodeCode Available	0
Adaptive loose optimization for robust question answering	May 6, 2023	Extractive Question-AnsweringMachine Reading Comprehension	CodeCode Available	0
Otter: A Multi-Modal Model with In-Context Instruction Tuning	May 5, 2023	GPUIn-Context Learning	CodeCode Available	4
Analysis of Visual Question Answering Algorithms with attention model	May 4, 2023	Question AnsweringVisual Question Answering	—Unverified	0
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content	May 3, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	0
Visual Reasoning: from State to Transformation	May 2, 2023	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available	1
An Empirical Study of Multimodal Model Merging	Apr 28, 2023	modelRetrieval	CodeCode Available	1
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment	Apr 28, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model	Apr 28, 2023	Instruction Followingmodel	CodeCode Available	5
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients	Apr 27, 2023	Quantum Machine LearningVisual Question Answering (VQA)	—Unverified	0
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality	Apr 27, 2023	Visual Question Answering (VQA)Zero-Shot Video Question Answer	CodeCode Available	4

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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