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 831–840 of 2167 papers

Title	Date	Tasks	Status	Score
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach	Oct 3, 2022	Referring ExpressionRobot Manipulation	CodeCode Available	5
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions	Nov 20, 2023	Question AnsweringVisual Question Answering	CodeCode Available	5
FigureQA: An Annotated Figure Dataset for Visual Reasoning	Oct 19, 2017	BIG-bench Machine LearningChart Question Answering	CodeCode Available	5
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering	May 21, 2015	Question AnsweringSentence	CodeCode Available	5
Modularized Zero-shot VQA with Pre-trained Models	May 27, 2023	object-detectionObject Detection	CodeCode Available	5
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	Sep 21, 2023	Cross-Modal RetrievalImage Captioning	CodeCode Available	5
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization	Dec 20, 2024	Compositional Generalization (AVG)Novel Concepts	CodeCode Available	5
Few-Shot Multimodal Explanation for Visual Question Answering	Oct 28, 2024	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available	5
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	CodeCode Available	5
Federated Document Visual Question Answering: A Pilot Study	May 10, 2024	Federated LearningQuestion Answering	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