Learning affordance segmentation: An investigative study
Document Type
Conference Proceeding
Publication Title
2020 Digital Image Computing: Techniques and Applications, DICTA
Publisher
IEEE
School
School of Science
RAS ID
35490
Funders
Edith Cowan University
Abstract
Affordance segmentation aims at recognising, localising and segmenting affordances from images, enabling scene understanding of visual content in many applications in robotic perception. Supervised learning with deep networks has become very popular in affordance segmentation. However, very few studies have investigated the factors that contribute to improved learning of affordances. This investigation is essential to improve precision and balance cost-efficiency when learning affordance segmentation. In this paper, we address this task and identify two prime factors affecting precision of learning affordance segmentation: (1) The quality of features extracted from the classification module and (2) the dearth of information in the Region Proposal Network (RPN). Consequently, we replace the backbone classification model and introduce a novel multiple alignment strategy in the RPN. Our results obtained through extensive experimentation validate our contributions and outperform the state-of-the-art affordance segmentation models.
DOI
10.1109/DICTA51227.2020.9363390
Access Rights
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Comments
Minh, C. N. D., Gilani, S. Z., Islam, S. M. S., & Suter, D. (2020, November - December). Learning affordance segmentation: An investigative study [Paper Presentation]. 2020 Digital Image Computing: Techniques and Applications, (DICTA), Melbourne, Australia. https://doi.org/10.1109/DICTA51227.2020.9363390