Semantic web-mining and deep vision for lifelong object discovery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Valerio Basile
  • Elena Cabrio
  • Barbara Caputo

Colleges, School and Institutes

External organisations

  • Institut national de recherche en informatique et en automatique, WIMMICS, France
  • University de Roma, Sapienza, Italy

Abstract

Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic Web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.

Details

Original languageEnglish
Title of host publication2017 IEEE International Conference on Robotics and Automation (ICRA)
Publication statusPublished - 24 Jul 2017
Event2017 IEEE International Conference on Robotics and Automation (ICRA 2017) - Singapore
Duration: 29 May 20173 Jun 2017

Conference

Conference2017 IEEE International Conference on Robotics and Automation (ICRA 2017)
CitySingapore
Period29/05/173/06/17

Keywords

  • Semantics, Knowledge based systems, Three-dimensional displays, Visualization, Mobile robots, Service robots