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 1871–1880 of 2167 papers

Title	Date	Tasks	Status
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents	Nov 23, 2024	Question AnsweringRAG	CodeCode Available
Learning Convolutional Text Representations for Visual Question Answering	May 18, 2017	General Classificationimage-classification	CodeCode Available
Learning content and context with language bias for Visual Question Answering	Dec 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Learning Conditioned Graph Structures for Interpretable Visual Question Answering	Jun 19, 2018	Question AnsweringVisual Question Answering	CodeCode Available
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and Trustworthiness	Nov 29, 2024	Optical Character Recognition (OCR)Question Answering	CodeCode Available
P NP, at least in Visual Question Answering	Mar 26, 2020	Question AnsweringVisual Question Answering	CodeCode Available
Visual Reasoning with Multi-hop Feature Modulation	Aug 3, 2018	Question AnsweringVisual Dialog	CodeCode Available
Learning by Abstraction: The Neural State Machine	Jul 9, 2019	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering	Dec 12, 2016	Question AnsweringVisual Question Answering	CodeCode Available
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking	Apr 18, 2022	cross-modal alignmentDocument AI	CodeCode Available

Show:10 25 50

← PrevPage 188 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	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