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 681–690 of 2167 papers

Title	Date	Tasks	Status
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering	Jul 28, 2023	Question AnsweringVietnamese Visual Question Answering	—Unverified
An Analysis of Visual Question Answering Algorithms	Mar 28, 2017	Question AnsweringVisual Question Answering	—Unverified
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering	Jun 1, 2019	General Classificationimage-classification	—Unverified
DCVQE: A Hierarchical Transformer for Video Quality Assessment	Oct 10, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation	Oct 27, 2023	Image GenerationQuestion Answering	—Unverified
CLIPPO: Image-and-Language Understanding from Pixels Only	Dec 15, 2022	Contrastive Learningimage-classification	—Unverified
PlotQA: Reasoning over Scientific Plots	Sep 3, 2019	Chart Question AnsweringQuestion Answering	—Unverified
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge	Mar 9, 2016	General KnowledgeImage Captioning	—Unverified
Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction	Apr 24, 2025	Conformal PredictionHallucination	—Unverified
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs	Jul 3, 2024	Image CaptioningImage Generation	—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