Knowledge-Supervised Learning: Knowledge Consensus Constraints for Person Re-Identification

Li Wang, Baoyu Fan, Zhenhua Guo, Yaqian Zhao, Runze Zhang, Rengang Li, Weifeng Gong, Endong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The consensus of multiple views on the same data will provide extra regularization, thereby improving accuracy. Based on this idea, we proposed a novel Knowledge-Supervised Learning (KSL) method for person re-identification (Re-ID), which can improve the performance without introducing extra inference cost. Firstly, we introduce isomorphic auxiliary training strategy to conduct basic multiple views that simultaneously train multiple classifier heads of the same network on the same training data. The consensus constraints aim to maximize the agreement among multiple views. To introduce this regular constraint, inspired by knowledge distillation that paired branches can be trained collaboratively through mutual imitation learning. Three novel constraints losses are proposed to distill the knowledge that needs to be transferred across different branches: similarity of predicted classification probability for cosine space constraints, distance of embedding features for euclidean space constraints, hard sample mutual mining for hard sample space constraints. From different perspectives, these losses complement each other. Experiments on four mainstream Re-ID datasets show that a standard model with KSL method trained from scratch outperforms its ImageNet pre-training results by a clear margin. With KSL method, a lightweight model without ImageNet pre-training outperforms most large models. We expect that these discoveries can attract some attention from the current de facto paradigm of "pre-training and fine-tuning"in Re-ID task to the knowledge discovery during model training.

Original languageEnglish
Title of host publicationMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages1866-1874
Number of pages9
ISBN (Electronic)9781450386517
DOIs
Publication statusPublished - 17 Oct 2021
Externally publishedYes
Event29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China
Duration: 20 Oct 202124 Oct 2021

Publication series

NameMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia

Conference

Conference29th ACM International Conference on Multimedia, MM 2021
Country/TerritoryChina
CityVirtual, Online
Period20/10/2124/10/21

Keywords

  • consensus constraints
  • isomorphic auxiliary training
  • knowledge distillation
  • person retrieval

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