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 1141–1150 of 2167 papers

Title	Date	Tasks	Status
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization	Nov 1, 2023	Domain GeneralizationQuestion Answering	—Unverified
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis	Oct 31, 2023	DescriptiveMedical Image Analysis	—Unverified
Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery	Oct 29, 2023	Deep LearningMultimodal Deep Learning	CodeCode Available
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese	Oct 27, 2023	Information RetrievalNatural Language Queries	CodeCode Available
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation	Oct 27, 2023	Image GenerationQuestion Answering	—Unverified
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs	Oct 26, 2023	AttributeMachine Translation	CodeCode Available
Exploring Question Decomposition for Zero-Shot VQA	Oct 25, 2023	Question AnsweringVisual Question Answering	—Unverified
Geometry-Aware Video Quality Assessment for Dynamic Digital Human	Oct 24, 2023	AttributeVideo Quality Assessment	—Unverified
LXMERT Model Compression for Visual Question Answering	Oct 23, 2023	modelModel Compression	CodeCode Available
A Simple Baseline for Knowledge-Based Visual Question Answering	Oct 20, 2023	In-Context LearningQuestion Answering	CodeCode Available

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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