Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

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.

arXiv commentOral paper in ECCV 2020

References in corpus (2)

• Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
• Kervolutional Neural Networks

Cited by in corpus (5)

• AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
• Unsupervised Online Learning for Robotic Interestingness with Visual Memory
• AirCode: A Robust Object Encoding Method
• AirLine: Efficient Learnable Line Detection with Local Edge Voting
• AirObject: A Temporally Evolving Graph Embedding for Object Identification