Learning Affordance Segmentation: An Investigative Study
| Autor: | Syed Islam, Chau Nguyen Duc Minh, David Suter, Syed Zulqarnain Gilani |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Supervised learning Feature extraction 02 engineering and technology Image segmentation 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Visualization Market segmentation 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Affordance computer 0105 earth and related environmental sciences |
| Zdroj: | DICTA |
| DOI: | 10.1109/dicta51227.2020.9363390 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|