Multi-stage Deep Classifier Cascades for Open World Recognition

TL;DR

This paper introduces a multi-stage deep classifier cascade framework that incrementally learns new classes and updates features in open world recognition, addressing limitations of static models and handcrafted features.

Contribution

The paper presents a novel end-to-end dynamic cascade of classifiers that detects, learns, and updates features for new classes in real time, improving open world recognition.

Findings

Outperforms state-of-the-art methods on real-world datasets

Effectively detects and incorporates new classes incrementally

Updates discriminative features dynamically during learning

Abstract

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more challenging because: i) new classes unseen in the training phase can appear when predicting; ii) discriminative features need to evolve when new classes emerge in real time; and iii) instances in new classes may not follow the "independent and identically distributed" (iid) assumption. Most existing work only aims to detect the unknown classes and is incapable of continuing to learn newer classes. Although a few methods consider both detecting and including new classes, all are based on the predefined handcrafted features that cannot evolve and are out-of-date for characterizing emerging classes. Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features. The proposed method injects dynamic elements into the system by detecting instances from unknown classes, while at the same time incrementally updating the model to include the new classes. The resulting cascade tree grows by adding a new leaf node classifier once a new class is detected, and the discriminative features are updated via an end-to-end learning strategy. Experiments on two real-world datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.