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 921–930 of 2167 papers

Title	Date	Tasks	Status	Score
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments	Oct 8, 2024	DecoderQuestion Answering	CodeCode Available	5
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering	Dec 2, 2016	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering	Oct 1, 2022	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	5
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning	Jul 6, 2022	DiagnosticMulti-Task Learning	CodeCode Available	5
LXMERT Model Compression for Visual Question Answering	Oct 23, 2023	modelModel Compression	CodeCode Available	5
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base	Dec 16, 2023	cross-modal alignmentKnowledge Graphs	CodeCode Available	5
Locally Smoothed Neural Networks	Nov 22, 2017	Face VerificationQuestion Answering	CodeCode Available	5
Logical Implications for Visual Question Answering Consistency	Mar 16, 2023	Language ModelingLanguage Modelling	CodeCode Available	5
Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training	Mar 30, 2024	Contrastive LearningQuestion Answering	CodeCode Available	5
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models	Aug 26, 2024	Large Language ModelVideo Quality Assessment	CodeCode Available	5

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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