Abstract
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 fewshot 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