  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% to 10%.
