Semantic Web-Mining and Deep Vision for Lifelong Object Discovery

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.
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.inria.fr/hal-01524902
Contributor : Valerio Basile <>
Submitted on : Friday, May 19, 2017 - 9:41:20 AM
Last modification on : Monday, November 5, 2018 - 3:52:09 PM

File

young17icra.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01524902, version 1

Collections

Citation

Jay Young, Lars Kunze, Valerio Basile, Elena Cabrio, Nick Hawes, et al.. Semantic Web-Mining and Deep Vision for Lifelong Object Discovery. IEEE International Conference on Robotics and Automation (ICRA), May 2017, Singapore, Singapore. ⟨hal-01524902⟩

Share

Metrics

Record views

755

Files downloads

614