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 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.
Keywords
- Machine Learning: Deep Learning
- Machine Learning Applications: Applications of Supervised Learning
- Computer Vision: Computer Vision
