Curriculum learning of visual attribute clusters for multi-task classification

Nikolaos Sarafianos, Theodoros Giannakopoulos, Christophoros Nikou, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped after performing hierarchical clustering based on their correlation. The clusters of tasks are learned in a curriculum learning setup by transferring knowledge between clusters. The learning process within each cluster is performed in a multi-task classification setup. By leveraging the acquired knowledge, we speed-up the process and improve performance. We demonstrate the effectiveness of our method via ablation studies and a detailed analysis of the covariates, on a variety of publicly available datasets of humans standing with their full-body visible. Extensive experimentation has proven that the proposed approach boosts the performance by 4%–10%.

Original languageEnglish (US)
Pages (from-to)94-108
Number of pages15
JournalPattern Recognition
StatePublished - Aug 2018


  • Curriculum learning
  • Multi-task classification
  • Visual attributes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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