On-line object detection: a robotics challenge

Author:

Maiettini ElisaORCID,Pasquale Giulia,Rosasco Lorenzo,Natale Lorenzo

Funder

Air Force Office of Scientific Research

H2020 Marie Sklodowska-Curie Actions

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference53 articles.

1. Bajcsy, R., Aloimonos, Y., & Tsotsos, J. K. (2018). Revisiting active perception. Autonomous Robots, 42(2), 177–196.

2. Browatzki, B., Tikhanoff, V., Metta, G., Bülthoff, H. H., & Wallraven, C. (2012). Active object recognition on a humanoid robot. In 2012 IEEE international conference on robotics and automation, pp. 2021–2028.

3. Dai, J., Li, Y., He, K., & Sun, J. (2016). R-fcn: Object detection via region-based fully convolutional networks. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems 29 (pp. 379–387). Curran Associates Inc.

4. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014). Decaf: A deep convolutional activation feature for generic visual recognition. In Jebara, T. and Xing, E. P., (Eds.), Proceedings of the 31st international conference on machine Learning (ICML-14), pp. 647–655. JMLR workshop and conference proceedings.

5. Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98–136.

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