Semantic web-mining and deep vision for lifelong object discovery

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

Standard

Semantic web-mining and deep vision for lifelong object discovery. / Young, Jay; Kunze, Lars; Basile, Valerio ; Cabrio, Elena ; Hawes, Nicholas; Caputo, Barbara .

2017 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 2774-2779.

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

Harvard

Young, J, Kunze, L, Basile, V, Cabrio, E, Hawes, N & Caputo, B 2017, Semantic web-mining and deep vision for lifelong object discovery. in 2017 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE), pp. 2774-2779, 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), Singapore, 29/05/17. https://doi.org/10.1109/ICRA.2017.7989323

APA

Young, J., Kunze, L., Basile, V., Cabrio, E., Hawes, N., & Caputo, B. (2017). Semantic web-mining and deep vision for lifelong object discovery. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2774-2779). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICRA.2017.7989323

Vancouver

Young J, Kunze L, Basile V, Cabrio E, Hawes N, Caputo B. Semantic web-mining and deep vision for lifelong object discovery. In 2017 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 2774-2779 https://doi.org/10.1109/ICRA.2017.7989323

Author

Young, Jay ; Kunze, Lars ; Basile, Valerio ; Cabrio, Elena ; Hawes, Nicholas ; Caputo, Barbara . / Semantic web-mining and deep vision for lifelong object discovery. 2017 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 2774-2779

Bibtex

@inproceedings{dab0c8b0903f49349ea25a4941f687eb,
title = "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.",
keywords = "Semantics, Knowledge based systems, Three-dimensional displays, Visualization, Mobile robots, Service robots",
author = "Jay Young and Lars Kunze and Valerio Basile and Elena Cabrio and Nicholas Hawes and Barbara Caputo",
year = "2017",
month = jul,
day = "24",
doi = "10.1109/ICRA.2017.7989323",
language = "English",
isbn = "9781509046348 (PoD)",
pages = "2774--2779",
booktitle = "2017 IEEE International Conference on Robotics and Automation (ICRA)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
note = "2017 IEEE International Conference on Robotics and Automation (ICRA 2017) ; Conference date: 29-05-2017 Through 03-06-2017",

}

RIS

TY - GEN

T1 - Semantic web-mining and deep vision for lifelong object discovery

AU - Young, Jay

AU - Kunze, Lars

AU - Basile, Valerio

AU - Cabrio, Elena

AU - Hawes, Nicholas

AU - Caputo, Barbara

PY - 2017/7/24

Y1 - 2017/7/24

N2 - 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.

AB - 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.

KW - Semantics

KW - Knowledge based systems

KW - Three-dimensional displays

KW - Visualization

KW - Mobile robots

KW - Service robots

U2 - 10.1109/ICRA.2017.7989323

DO - 10.1109/ICRA.2017.7989323

M3 - Conference contribution

SN - 9781509046348 (PoD)

SP - 2774

EP - 2779

BT - 2017 IEEE International Conference on Robotics and Automation (ICRA)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2017 IEEE International Conference on Robotics and Automation (ICRA 2017)

Y2 - 29 May 2017 through 3 June 2017

ER -