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 601–610 of 2167 papers

Title	Date	Tasks	Status	Score
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering	Sep 23, 2020	Question AnsweringVisual Question Answering	CodeCode Available	5
Blind Prediction of Natural Video Quality	Jan 9, 2014	PredictionVideo Quality Assessment	CodeCode Available	5
A Neuro-Symbolic ASP Pipeline for Visual Question Answering	May 16, 2022	Question AnsweringVisual Question Answering	CodeCode Available	5
Multimodal Residual Learning for Visual QA	Jun 5, 2016	Multiple-choiceQuestion Answering	CodeCode Available	5
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents	Nov 23, 2024	Question AnsweringRAG	CodeCode Available	5
Biomedical Visual Instruction Tuning with Clinician Preference Alignment	Jun 19, 2024	Instruction FollowingVisual Question Answering (VQA)	CodeCode Available	5
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling	Feb 20, 2025	DecoderGPU	CodeCode Available	5
BioD2C: A Dual-level Semantic Consistency Constraint Framework for Biomedical VQA	Mar 4, 2025	Medical DiagnosisQuestion Answering	CodeCode Available	5
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering	Dec 19, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	5
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	5

Show:10 25 50

← PrevPage 61 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