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 921–930 of 2167 papers

Title	Date	Tasks	Status	Hype
An Empirical Study of Multimodal Model Merging	Apr 28, 2023	modelRetrieval	CodeCode Available	1
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment	Apr 28, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model	Apr 28, 2023	Instruction Followingmodel	CodeCode Available	5
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients	Apr 27, 2023	Quantum Machine LearningVisual Question Answering (VQA)	—Unverified	0
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality	Apr 27, 2023	Visual Question Answering (VQA)Zero-Shot Video Question Answer	CodeCode Available	4
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering	Apr 26, 2023	DecoderKnowledge Distillation	CodeCode Available	1
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Making Video Quality Assessment Models Robust to Bit Depth	Apr 25, 2023	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models	Apr 20, 2023	Image DescriptionLanguage Modelling	CodeCode Available	7
SurgicalGPT: End-to-End Language-Vision GPT for Visual Question Answering in Surgery	Apr 19, 2023	Question AnsweringScene Segmentation	CodeCode Available	1

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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