Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment

Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
Title:
Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
Other Titles:
2020 IEEE International Conference on Image Processing (ICIP)
Publication Date:
30 September 2020
Citation:
J. Duan, S. Yu, H. L. Tan and C. Tan, "Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment," 2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 1566-1570, doi: 10.1109/ICIP40778.2020.9191324.
Abstract:
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for artificial agents, a significant remaining bottleneck is the dearth of large-scale comprehensive task-based datasets. In this paper, we present ActioNet, an interactive end-to-end platform for data collection and augmentation of task-based dataset in 3D environment. Using ActioNet, we collected a large-scale comprehensive task-based dataset, comprising over 3000 hierarchical task structures and videos. Using the hierarchical task structures, the videos are further augmented across 50 different scenes to give over 150,000 video. To our knowledge, ActioNet is the first interactive end-to-end platform for such task-based dataset generation and the accompanying dataset is the largest task-based dataset of such comprehensive nature.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046

This research / project is supported by the National Research Foundation, Singapore - NRF-ISF Joint Call
Grant Reference no. : NRF2015-NRF-ISF001-2541
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
1522-4880
ISBN:
978-1-7281-6395-6
978-1-7281-6394-9
978-1-7281-6396-3
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