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 1761–1770 of 2167 papers

Title	Date	Tasks	Status
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering	Jun 16, 2020	Question AnsweringVisual Question Answering	—Unverified
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA	Jan 29, 2024	BenchmarkingImage Comprehension	—Unverified
Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering	Dec 24, 2024	Question AnsweringVisual Question Answering	—Unverified
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes	Jun 4, 2023	Common Sense ReasoningQuestion Answering	—Unverified
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering	Dec 20, 2023	Question AnsweringVisual Question Answering	—Unverified
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling	Mar 10, 2023	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	—Unverified
Multi-grained Attention with Object-level Grounding for Visual Question Answering	Jul 1, 2019	ObjectQuestion Answering	—Unverified
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering	Jan 3, 2020	Question AnsweringVideo Description	—Unverified
Multi-Level Attention Networks for Visual Question Answering	Jul 1, 2017	Question AnsweringVisual Question Answering	—Unverified
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment	Jan 6, 2025	Video Quality AssessmentVisual Question Answering (VQA)	—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