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 2091–2100 of 2167 papers

Title	Date	Tasks	Status
Zero-shot Visual Question Answering with Language Model Feedback	May 26, 2023	Language ModelingLanguage Modelling	CodeCode Available
Fully Authentic Visual Question Answering Dataset from Online Communities	Nov 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available
Analyzing the Behavior of Visual Question Answering Models	Jun 23, 2016	Question AnsweringVisual Question Answering	CodeCode Available
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms	Aug 29, 2018	Community Question AnsweringGeneral Classification	CodeCode Available
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models	Dec 21, 2022	Question AnsweringVisual Question Answering	CodeCode Available
A simple neural network module for relational reasoning	Jun 5, 2017	Image Retrieval with Multi-Modal QueryQuestion Answering	CodeCode Available
Visually Grounded VQA by Lattice-based Retrieval	Nov 15, 2022	Information RetrievalQuestion Answering	CodeCode Available
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering	Dec 19, 2023	Image RetrievalQuestion Answering	CodeCode Available
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment	Aug 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering	May 27, 2025	BenchmarkingQuestion Answering	CodeCode Available

Show:10 25 50

← PrevPage 210 of 217Next →

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