Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Liuyu Xiang, Xiaoming Jin, Guiguang Ding, Jungong Han, Leida Li

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 3912-3918. https://doi.org/10.24963/ijcai.2019/543

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