  Pedestrian attribute recognition has received increasing attention due to its
important role in video surveillance applications. However, most existing
methods are designed for a fixed set of attributes. They are unable to handle
the incremental few-shot learning scenario, i.e. adapting a well-trained model
to newly added attributes with scarce data, which commonly exists in the real
world. In this work, we present a meta learning based method to address this
issue. The core of our framework is a meta architecture capable of
disentangling multiple attribute information and generalizing rapidly to new
coming attributes. By conducting extensive experiments on the benchmark dataset
PETA and RAP under the incremental few-shot setting, we show that our method is
able to perform the task with competitive performances and low resource
requirements.
