Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
By Chen Wang, Wenshan Wang, Yuheng Qiu, Yafei Hu, Sebastian A. Scherer. 2020
24 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Sebastian A. Scherer
- Yuheng Qiu
- Yafei Hu
- Wenshan Wang
- Ashish Kapoor
- Chen Wang
- Luc Van Gool
- Dacheng Tao
Citations in the corpus
- Lifelong Graph Learning (2020, neutral): “A sparse writing protocol is introduced to a memory module [42], ensuring that only a few memory spaces is affected during training.”
- TartanAir: A Dataset to Push the Limits of Visual SLAM (2020, background): “We have set up 30 environments, which cover a wide range of scenarios in the real world, from structured urban, indoor scenes to unstructured natural environments [27] (Fig.”