Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120134
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Type: Conference paper
Title: Semantic segmentation from limited training data
Author: Milan, A.
Pham, T.
Vijay, K.
Morrison, D.
Tow, A.
Liu, L.
Erskine, J.
Grinover, R.
Gurman, A.
Hunn, T.
Kelly-Boxall, N.
Lee, D.
McTaggart, M.
Rallos, G.
Razjigaev, A.
Rowntree, T.
Shen, T.
Smith, R.
Wade-McCue, S.
Zhuang, Z.
Lehnert, C.
Lin, G.
Reid, I.
Corke, P.
Leitner, J.
Citation: 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 / vol.abs/1709.07665, pp.1908-1915
Publisher: IEEE
Issue Date: 2018
Series/Report no.: IEEE International Conference on Robotics and Automation ICRA
ISBN: 9781538630815
ISSN: 1050-4729
2577-087X
Conference Name: IEEE International Conference on Robotics and Automation (ICRA) (21 May 2018 - 25 May 2018 : Brisbane, Australia)
Statement of
Responsibility: 
A. Milan, T. Pham, K. Vijay, D. Morrison, A.W. Tow, L. Liu, J. Erskine, R. Grinover, A. Gurman, T. Hunn, N. Kelly-Boxall, D. Lee, M. McTaggart, G. Rallos, A. Razjigaev, T. Rowntree, T. Shen, R. Smith, S. Wade-McCue, Z. Zhuang, C. Lehnert, G. Lin, I. Reid, P. Corke, and J. Leitner
Abstract: We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.
Rights: © 2018 IEEE
RMID: 0030111960
DOI: 10.1109/ICRA.2018.8461082
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
Appears in Collections:Computer Science publications

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