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 826–850 of 2167 papers

Title	Date	Tasks	Status	Hype
Med-Flamingo: a Multimodal Medical Few-shot Learner	Jul 27, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	2
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
LOIS: Looking Out of Instance Semantics for Visual Question Answering	Jul 26, 2023	Question AnsweringVisual Question Answering	—Unverified	0
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
Robust Visual Question Answering: Datasets, Methods, and Future Challenges	Jul 21, 2023	Question AnsweringVisual Question Answering	—Unverified	0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge	Jul 19, 2023	DeblurringImage Restoration	—Unverified	0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading	Jul 19, 2023	Medical Image AnalysisQuestion Answering	—Unverified	0
Explaining Autonomous Driving Actions with Visual Question Answering	Jul 19, 2023	Autonomous DrivingAutonomous Vehicles	CodeCode Available	1
Generative Visual Question Answering	Jul 18, 2023	Generative Visual Question AnsweringQuestion Answering	—Unverified	0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving	Jul 18, 2023	Autonomous DrivingModel Selection	CodeCode Available	0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation	Jul 18, 2023	Image GenerationQuestion Answering	—Unverified	0
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting	Jul 11, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
Emu: Generative Pretraining in Multimodality	Jul 11, 2023	Image CaptioningImage Generation	CodeCode Available	3
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery	Jul 11, 2023	Question AnsweringScene Understanding	CodeCode Available	1
Subjective and Objective Audio-Visual Quality Assessment for User Generated Content	Jul 10, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	0
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback	Jul 10, 2023	Image GenerationVisual Question Answering (VQA)	—Unverified	0
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models	Jul 9, 2023	Question AnsweringTGIF-Frame	CodeCode Available	1
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest	Jul 7, 2023	AttributeCommon Sense Reasoning	CodeCode Available	2
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering	Jul 6, 2023	DiagnosticImage Enhancement	—Unverified	0
Localized Questions in Medical Visual Question Answering	Jul 3, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding	Jul 3, 2023	Image-text matchingSentence	CodeCode Available	1
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment	Jul 1, 2023	Language ModelingLanguage Modelling	—Unverified	0
Lightweight Recurrent Cross-modal Encoder for Video Question Answering	Jun 30, 2023	Action RecognitionQuestion Answering	CodeCode Available	0
Multimodal Prompt Retrieval for Generative Visual Question Answering	Jun 30, 2023	Domain AdaptationGenerative Visual Question Answering	CodeCode Available	1
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning	Jun 30, 2023	Causal InferenceMedical Report Generation	CodeCode Available	3

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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