Hierarchical Open-Set Object Detection in Unseen Data

Abstract

In this paper, we propose an open-set object detection framework based on a dynamic hierarchical structure with incremental learning capabilities for unseen object classes. We were motivated by the observation that deep features extracted from visual objects show a strong hierarchical clustering property. The hierarchical feature model (HFM) was used to learn a new object class by using collaborative sampling (CS), and open-set-aware active semi-supervised learning (ASSL) algorithms. We divided object proposals into superclasses by using the agglomerative clustering algorithm. Data samples in each superclass node were classified into multiple augmented class nodes instead of directly associating with regular object classes. One or more augmented class nodes are related to a regular object class, and each augmented class has only one superclass. Object proposals from inexperienced data distribution are assigned to an augmented class node. Dynamic HFM nodes in the decision path are assembled to constitute an ensemble prediction, and the new augmented object is associated with a new regular object class. Our experimental results showed that the proposed method uses standard benchmark datasets such as PASCAL VOC, MS COCO, ILSVRC DET, and local datasets to perform better than state-of-the-art techniques.