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 381–390 of 2167 papers

Title	Date	Tasks	Status	Hype
Video Instruction Tuning With Synthetic Data	Oct 3, 2024	3D Question Answering (3D-QA)	—Unverified	0
Backdooring Vision-Language Models with Out-Of-Distribution Data	Oct 2, 2024	Image CaptioningImage to text	—Unverified	0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities	Oct 2, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Unleashing the Potentials of Likelihood Composition for Multi-modal Language Models	Oct 1, 2024	Question AnsweringVisual Question Answering	CodeCode Available	0
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data	Oct 1, 2024	Code GenerationLogical Reasoning	CodeCode Available	0
A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning	Oct 1, 2024	Common Sense ReasoningDeepFake Detection	CodeCode Available	1
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks	Oct 1, 2024	BenchmarkingFairness	—Unverified	0
T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition	Sep 29, 2024	In-Context LearningQuestion Answering	CodeCode Available	1
Visual Question Decomposition on Multimodal Large Language Models	Sep 28, 2024	Visual Question Answering (VQA)	—Unverified	0
TrojVLM: Backdoor Attack Against Vision Language Models	Sep 28, 2024	Backdoor AttackImage Captioning	—Unverified	0

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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