Mancs: A Multi-task Attentional Network with
Curriculum Sampling for Person
Re-identification
Abstract. We propose a novel deep network called Mancs that solves
the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem
and properly sampling for the ranking loss to obtain more stable person
representation. Technically, we contribute a novel fully attentional block
which is deeply supervised and can be plugged into any CNN, and a novel
curriculum sampling method which is effective for training ranking losses. The learning tasks are integrated into a unified framework and jointly
optimized. Experiments have been carried out on Market1501, CUHK03
and DukeMTMC. All the results show that Mancs can significantly outperform the previous state-of-the-arts. In addition, the effectiveness of
the newly proposed ideas has been confirmed by extensive ablation studies