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 981–990 of 2167 papers

Title	Date	Tasks	Status
Priorformer: A UGC-VQA Method with content and distortion priors	Jun 24, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis	Jun 21, 2024	AttributeMedical Visual Question Answering	—Unverified
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning	Jun 20, 2024	Image ComprehensionQuestion Answering	CodeCode Available
Biomedical Visual Instruction Tuning with Clinician Preference Alignment	Jun 19, 2024	Instruction FollowingVisual Question Answering (VQA)	CodeCode Available
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQA	Jun 18, 2024	Question AnsweringVisual Question Answering	CodeCode Available
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model	Jun 15, 2024	Question AnsweringVideo Understanding	CodeCode Available
What is the Visual Cognition Gap between Humans and Multimodal LLMs?	Jun 14, 2024	object-detectionObject Detection	CodeCode Available
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models	Jun 14, 2024	DecoderKnowledge Graphs	—Unverified
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns	Jun 13, 2024	Autonomous DrivingQuestion Answering	—Unverified
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark	Jun 10, 2024	DiversityQuestion Answering	—Unverified

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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