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 1291–1300 of 2167 papers

Title	Date	Tasks	Status
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training	May 21, 2021	Question AnsweringRelation	—Unverified
Probing the Role of Positional Information in Vision-Language Models	Jan 16, 2022	Contrastive LearningImage-text matching	—Unverified
Probing Visual Language Priors in VLMs	Dec 31, 2024	Question AnsweringVisual Question Answering	—Unverified
ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data	Jul 17, 2024	Question AnsweringVisual Question Answering	—Unverified
Progressive Attention Memory Network for Movie Story Question Answering	Apr 18, 2019	Question AnsweringVideo Story QA	—Unverified
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning	May 21, 2025	AllVisual Question Answering (VQA)	—Unverified
Prompt-based Personalized Federated Learning for Medical Visual Question Answering	Feb 15, 2024	Federated LearningMedical Visual Question Answering	—Unverified
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3	Jan 1, 2023	Image CaptioningQuestion Answering	—Unverified
Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering	Dec 22, 2024	Question AnsweringVisual Question Answering	—Unverified
Prompt Tuning for Generative Multimodal Pretrained Models	Aug 4, 2022	Image CaptioningVisual Entailment	—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