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 281–290 of 2167 papers

Title	Date	Tasks	Status	Hype
Detecting and Preventing Hallucinations in Large Vision Language Models	Aug 11, 2023	16kHallucination	CodeCode Available	1
StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability	Aug 9, 2023	Optical Flow EstimationVideo Quality Assessment	CodeCode Available	1
SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs	Aug 7, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1
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
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
Explaining Autonomous Driving Actions with Visual Question Answering	Jul 19, 2023	Autonomous DrivingAutonomous Vehicles	CodeCode Available	1
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting	Jul 11, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
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
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models	Jul 9, 2023	Question AnsweringTGIF-Frame	CodeCode Available	1
Localized Questions in Medical Visual Question Answering	Jul 3, 2023	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1

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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