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arXiv · 1906.00330

Incremental Few-Shot Learning for Pedestrian Attribute Recognition

Liuyu Xiang·Xiaoming Jin·Guiguang Ding·Jungong Han·Leida Li
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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.

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Liuyu Xiang, Xiaoming Jin, Guiguang Ding, Jungong Han, Leida Li. 2019-06-02. Incremental Few-Shot Learning for Pedestrian Attribute Recognition. https://arxiv.org/abs/1906.00330

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