OpenGCD: Assisting Open World Recognition with Generalized Category
  Discovery
Fulin Gao Weimin Zhong Zhixing Cao Xin Peng Zhi Li
A desirable open world recognition (OWR) system requires performing three
tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes
seen during training) and rejecting the unknown (unseen$/$novel classes)
online; (2) Grouping and labeling these unknown as novel known classes; (3)
Incremental learning (IL), i.e., continual learning these novel classes and
retaining the memory of old classes. Ideally, all of these steps should be
automated. However, existing methods mostly assume that the second task is
completely done manually. To bridge this gap, we propose OpenGCD that combines
three key ideas to solve the above problems sequentially: (a) We score the
origin of instances (unknown or specifically known) based on the uncertainty of
the classifier's prediction; (b) For the first time, we introduce generalized
category discovery (GCD) techniques in OWR to assist humans in grouping
unlabeled data; (c) For the smooth execution of IL and GCD, we retain an equal
number of informative exemplars for each class with diversity as the goal.
Moreover, we present a new performance evaluation metric for GCD called
harmonic clustering accuracy. Experiments on two standard classification
benchmarks and a challenging dataset demonstrate that OpenGCD not only offers
excellent compatibility but also substantially outperforms other baselines.
Code: https://github.com/Fulin-Gao/OpenGCD.