Discriminative Self-Paced Group-Metric Adaptation for Online Visual Identification

Jiahuan Zhou, Bing Su*, Ying Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Existing solutions to instance-level visual identification usually aim to learn faithful and discriminative feature extractors from offline training data and directly use them for the unseen online testing data. However, their performance is largely limited due to the severe distribution shifting issue between training and testing samples. Therefore, we propose a novel online group-metric adaptation model to adapt the offline learned identification models for the online data by learning a series of metrics for all sharing-subsets. Each sharing-subset is obtained from the proposed novel frequent sharing-subset mining module and contains a group of testing samples that share strong visual similarity relationships to each other. Furthermore, to handle potentially large-scale testing samples, we introduce self-paced learning (SPL) to gradually include samples into adaptation from easy to difficult which elaborately simulates the learning principle of humans. Unlike existing online visual identification methods, our model simultaneously takes both the sample-specific discriminant and the set-based visual similarity among testing samples into consideration. Our method is generally suitable to any off-the-shelf offline learned visual identification baselines for online performance improvement which can be verified by extensive experiments on several widely-used visual identification benchmarks.

Original languageEnglish (US)
Pages (from-to)4368-4383
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number4
StatePublished - Apr 1 2023


  • Learning from sharing
  • frequent pattern mining
  • online adaptation
  • person re-identification
  • self-paced learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics


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