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 251–260 of 2167 papers

Title	Date	Tasks	Status	Hype
ErgoChat: a Visual Query System for the Ergonomic Risk Assessment of Construction Workers	Dec 27, 2024	Image CaptioningQuestion Answering	—Unverified	0
FineVQ: Fine-Grained User Generated Content Video Quality Assessment	Dec 26, 2024	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation	Dec 24, 2024	Image CaptioningImage Generation	CodeCode Available	2
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question Answering	Dec 24, 2024	Explanatory Visual Question AnsweringMultimodal Reasoning	CodeCode Available	0
Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering	Dec 24, 2024	Question AnsweringVisual Question Answering	—Unverified	0
HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images	Dec 24, 2024	Optical Character Recognition (OCR)Question Answering	—Unverified	0
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization	Dec 24, 2024	In-Context LearningQuestion Answering	—Unverified	0
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective	Dec 23, 2024	Question AnsweringVisual Question Answering	CodeCode Available	0
Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering	Dec 22, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Application of Multimodal Large Language Models in Autonomous Driving	Dec 21, 2024	Autonomous DrivingDecision Making	—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	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