Title: Deep Self-Paced Learning for Person Re-Identification,
Abstract: Person re-identification (Re-ID) usually suffers from noisy samples with
background clutter and mutual occlusion, which makes it extremely difficult to
distinguish different individuals across the disjoint camera views. In this
paper, we propose a novel deep self-paced learning (DSPL) algorithm to
alleviate this problem, in which we apply a self-paced constraint and symmetric
regularization to help the relative distance metric training the deep neural
network, so as to learn the stable and discriminative features for person
Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive
the adaptive weights to samples based on both the training loss and model age.
As a result, the high-confidence fidelity samples will be emphasized and the
low-confidence noisy samples will be suppressed at early stage of the whole
training process. Such a learning regime is naturally implemented under a
self-paced learning (SPL) framework, in which samples weights are adaptively
updated based on both model age and sample loss using an alternative
optimization method. Secondly, we introduce a symmetric regularizer term to
revise the asymmetric gradient back-propagation derived by the relative
distance metric, so as to simultaneously minimize the intra-class distance and
maximize the inter-class distance in each triplet unit. Finally, we build a
part-based deep neural network, in which the features of different body parts
are first discriminately learned in the lower convolutional layers and then
fused in the higher fully connected layers. Experiments on several benchmark
datasets have demonstrated the superior performance of our method as compared
with the state-of-the-art approaches.