Learning Affordance Segmentation: An Investigative Study

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.