Towards lifelong object recognition : A dataset and benchmark

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Qihan Yang
  • Xinyue Hao
  • Ivan Mashkin
  • Ka Shun Kei
  • Dong Qiang
  • Vincenzo Lomonaco
  • Xuesong Shi
  • Zhengwei Wang
  • Yao Guo
  • Yimin Zhang
  • Fei Qiao

Detail(s)

Original languageEnglish
Article number108819
Journal / PublicationPattern Recognition
Volume130
Online published27 May 2022
Publication statusPublished - Oct 2022

Abstract

Lifelong learning algorithms aim to enable robots to handle open-set and detrimental conditions, and yet there is a lack of adequate datasets with diverse factors for benchmarking. In this work, we constructed and released a lifelong learning robotic vision dataset, OpenLORIS-Object. This dataset was collected by RGB-D camera capturing dynamic environment in daily life scenarios with diverse factors, including illumination, occlusion, object pixel size and clutter, of quantified difficulty levels. To the best of our knowledge, this is an unique real-world dataset for robotic vision with independent and quantifiable environmental factors, which are currently unaccounted for in other lifelong learning datasets such as CORe50 and NICO. We tested 9 state-of-the-art algorithms with 4 evaluation metrics over the dataset in Domain Incremental Learning, Task Incremental Learning, and Class Incremental Learning scenarios. The results demonstrate that these existing algorithms are insufficient to handle lifelong learning task in dynamic environments. Our dataset and benchmarks are now publicly available at this website.2

Research Area(s)

  • Robotic vision, Continual learning, Lifelong learning, Object recognition

Citation Format(s)

Towards lifelong object recognition : A dataset and benchmark. / Lan, Chuanlin; Feng, Fan; Liu, Qi; She, Qi; Yang, Qihan; Hao, Xinyue; Mashkin, Ivan; Kei, Ka Shun; Qiang, Dong; Lomonaco, Vincenzo; Shi, Xuesong; Wang, Zhengwei; Guo, Yao; Zhang, Yimin; Qiao, Fei; Chan, Rosa H.M.

In: Pattern Recognition, Vol. 130, 108819, 10.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review