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 21–30 of 2167 papers

Title	Date	Tasks	Status	Hype
Otter: A Multi-Modal Model with In-Context Instruction Tuning	May 5, 2023	GPUIn-Context Learning	CodeCode Available	4
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models	Aug 2, 2023	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	4
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality	Apr 27, 2023	Visual Question Answering (VQA)Zero-Shot Video Question Answer	CodeCode Available	4
Exploring the Capabilities of Large Multimodal Models on Dense Text	May 9, 2024	Prompt EngineeringVisual Question Answering (VQA)	CodeCode Available	4
Multi-label Cluster Discrimination for Visual Representation Learning	Jul 24, 2024	Contrastive LearningImage-text Retrieval	CodeCode Available	4
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	Jan 30, 2023	Generative Visual Question AnsweringImage Captioning	CodeCode Available	4
Flamingo: a Visual Language Model for Few-Shot Learning	Apr 29, 2022	Few-Shot LearningGenerative Visual Question Answering	CodeCode Available	4
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning	May 2, 2024	Autonomous Drivingcounterfactual	CodeCode Available	4
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video	Feb 1, 2023	Action ClassificationImage Classification	CodeCode Available	4
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration	Nov 7, 2023	1 Image, 2*2 StitchingDecoder	CodeCode Available	4

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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	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