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 771–780 of 2167 papers

Title	Date	Tasks	Status	Score
A simple neural network module for relational reasoning	Jun 5, 2017	Image Retrieval with Multi-Modal QueryQuestion Answering	CodeCode Available	5
HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language	May 28, 2023	Machine TranslationMultimodal Machine Translation	CodeCode Available	5
Modularized Zero-shot VQA with Pre-trained Models	May 27, 2023	object-detectionObject Detection	CodeCode Available	5
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models	Aug 2, 2017	Question AnsweringVisual Question Answering	CodeCode Available	5
CLIPVQA:Video Quality Assessment via CLIP	Jul 6, 2024	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	5
A Simple Baseline for Knowledge-Based Visual Question Answering	Oct 20, 2023	In-Context LearningQuestion Answering	CodeCode Available	5
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models	Dec 21, 2022	Question AnsweringVisual Question Answering	CodeCode Available	5
Hierarchical Deep Multi-modal Network for Medical Visual Question Answering	Sep 27, 2020	DescriptiveMedical Visual Question Answering	CodeCode Available	5
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering	Jun 6, 2019	Question AnsweringVideo Question Answering	CodeCode Available	5
Modeling Relationships in Referential Expressions with Compositional Modular Networks	Nov 30, 2016	Visual Question Answering (VQA)	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