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VIVID: Virtual Environment for Visual Deep Learning

Authors:

Ming-Syan ChenAuthors Info & Claims

MM '18: Proceedings of the 26th ACM international conference on Multimedia

Pages 1356 - 1359

https://doi.org/10.1145/3240508.3243653

Published: 15 October 2018 Publication History

Abstract

Due to the advances in deep reinforcement learning and the demand of large training data, virtual-to-real learning has gained lots of attention from computer vision community recently. As state-of-the-art 3D engines can generate photo-realistic images suitable for training deep neural networks, researchers have been gradually applied 3D virtual environment to learn different tasks including autonomous driving, collision avoidance, and image segmentation, to name a few. Although there are already many open-source simulation environments readily available, most of them either provide small scenes or have limited interactions with objects in the environment. To facilitate visual recognition learning, we present a new Virtual Environment for Visual Deep Learning (VIVID), which offers large-scale diversified indoor and outdoor scenes. Moreover, VIVID leverages the advanced human skeleton system, which enables us to simulate numerous complex human actions. VIVID has a wide range of applications and can be used for learning indoor navigation, action recognition, event detection, etc. We also release several deep learning examples in Python to demonstrate the capabilities and advantages of our system.

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Cited By

Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Luo HLuo JVasilakos A(2024)BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language modelsNeurocomputing10.1016/j.neucom.2024.128089599(128089)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128089
Yao DZhu MZhu HCai WZhou L(2024)Integrating synthetic datasets with CLIP semantic insights for single image localization advancementsISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.10.027218(198-213)Online publication date: Dec-2024
https://doi.org/10.1016/j.isprsjprs.2024.10.027
Show More Cited By

Index Terms

VIVID: Virtual Environment for Visual Deep Learning
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Virtual reality
  2. Machine learning

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Information

Published In

cover image ACM Conferences

MM '18: Proceedings of the 26th ACM international conference on Multimedia

October 2018

2167 pages

ISBN:9781450356657

DOI:10.1145/3240508

General Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Kyoung Mu Lee
Seoul National University, Korea
,
Jiebo Luo
University of Rochester, USA
,
Wenwu Zhu
Tsinghua University, China
,
Program Chairs:
Hyeran Byun
Yonsei University, Korea
,
Chang Wen Chen
State Univ. Of New York at Buffalo, USA
,
Rainer Lienhart
University of Augsburg, Germany
,
Tao Mei
JD AI, China

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

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MM '18

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MM '18: ACM Multimedia Conference

October 22 - 26, 2018

Seoul, Republic of Korea

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MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Shen MTan ZNiyato DLiu YKang JXiong ZZhu LWang WShen X(2024)Artificial Intelligence for Web 3.0: A Comprehensive SurveyACM Computing Surveys10.1145/365728456:10(1-39)Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3657284
Luo HLuo JVasilakos A(2024)BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language modelsNeurocomputing10.1016/j.neucom.2024.128089599(128089)Online publication date: Sep-2024
https://doi.org/10.1016/j.neucom.2024.128089
Yao DZhu MZhu HCai WZhou L(2024)Integrating synthetic datasets with CLIP semantic insights for single image localization advancementsISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.10.027218(198-213)Online publication date: Dec-2024
https://doi.org/10.1016/j.isprsjprs.2024.10.027
Shen MTang XWang WZhu LShen MTang XWang WZhu L(2024)Introduction of Web 3.0Security and Privacy in Web 3.010.1007/978-981-97-5752-7_1(1-14)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-981-97-5752-7_1
Madan NSiemon MGjerde MPetersson BGrotuzas AEsbensen MNikolov IPhilipsen MNasrollahi KMoeslund T(2023)ThermalSynth: A Novel Approach for Generating Synthetic Thermal Human Scenarios2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW58289.2023.00018(130-139)Online publication date: Jan-2023
https://doi.org/10.1109/WACVW58289.2023.00018
Lai KChung YSu JLai CHuang Y(2023)AI Wings: An AIoT Drone System for Commanding ArduPilot UAVsIEEE Systems Journal10.1109/JSYST.2022.318901117:2(2213-2224)Online publication date: Jun-2023
https://doi.org/10.1109/JSYST.2022.3189011
You WHuang CHu KLiu TLai K(2023)Augmented Reality for Real Object Detection2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)10.1109/ICCE-Taiwan58799.2023.10227067(803-804)Online publication date: 17-Jul-2023
https://doi.org/10.1109/ICCE-Taiwan58799.2023.10227067
Gokce Narin N(2023)The Role of Artificial Intelligence and Robotic Solution Technologies in Metaverse DesignMetaverse10.1007/978-981-99-4641-9_4(45-63)Online publication date: 13-Oct-2023
https://doi.org/10.1007/978-981-99-4641-9_4
Li JCai SYang QHuang H(2023)How to Enrich Metaverse? Blockchains, AI, and Digital TwinFrom Blockchain to Web3 & Metaverse10.1007/978-981-99-3648-9_2(27-61)Online publication date: 25-May-2023
https://doi.org/10.1007/978-981-99-3648-9_2
Yang QZhao YHuang HXiong ZKang JZheng Z(2022)Fusing Blockchain and AI With Metaverse: A SurveyIEEE Open Journal of the Computer Society10.1109/OJCS.2022.31882493(122-136)Online publication date: 2022
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