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 111–120 of 2167 papers

Title	Date	Tasks	Status	Hype
KVQ: Kwai Video Quality Assessment for Short-form Videos	Feb 11, 2024	FormVideo Quality Assessment	CodeCode Available	2
Learning to Compose Dynamic Tree Structures for Visual Contexts	Dec 5, 2018	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	2
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts	May 9, 2024	Image CaptioningInstruction Following	CodeCode Available	2
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents	Mar 13, 2023	image-classificationImage Classification	CodeCode Available	2
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering	Mar 3, 2023	Language ModellingLarge Language Model	CodeCode Available	2
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks	May 24, 2024	Language ModelingLanguage Modelling	CodeCode Available	2
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest	Jul 7, 2023	AttributeCommon Sense Reasoning	CodeCode Available	2
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions	Aug 19, 2023	MMEOptical Character Recognition (OCR)	CodeCode Available	2
Retrieval Augmented Visual Question Answering with Outside Knowledge	Oct 7, 2022	Answer GenerationDiagnostic	CodeCode Available	2
GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI	Aug 6, 2024	Question AnsweringVisual Question Answering	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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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