Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

Chen Wang, Wenshan Wang, Yuheng Qiu, Yafei Hu, Sebastian Scherer

ECCV 2020

doi:10.1007/978-3-030-58536-5_4 /eccv/2020/wang2020eccv-visual/

Abstract

In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as autonomous exploration and decision making. Inspired by industrial demands, we first propose a novel translation-invariant visual memory for recalling and identifying interesting scenes, then design a three-stage architecture of long-term, short-term, and online learning. This enables our system to learn human-like experience, environmental knowledge, and online adaption, respectively. Our approach achieves much higher accuracy than the state-of-the-art algorithms on challenging robotic interestingness datasets.

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Cite

Text

Wang et al. "Visual Memorability for Robotic Interestingness via Unsupervised Online Learning." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58536-5_4

Markdown

[Wang et al. "Visual Memorability for Robotic Interestingness via Unsupervised Online Learning." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/wang2020eccv-visual/) doi:10.1007/978-3-030-58536-5_4

BibTeX

@inproceedings{wang2020eccv-visual,
  title     = {{Visual Memorability for Robotic Interestingness via Unsupervised Online Learning}},
  author    = {Wang, Chen and Wang, Wenshan and Qiu, Yuheng and Hu, Yafei and Scherer, Sebastian},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2020},
  doi       = {10.1007/978-3-030-58536-5_4},
  url       = {https://mlanthology.org/eccv/2020/wang2020eccv-visual/}
}