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.
Highlights
There have been many advances in object detection technology since the deep-learning breakthrough achieved by Krizhevsky et al in 2012 [1]
When we applied dynamic hierarchical feature model (HFM), we could see that applying active semi-supervised learning (ASSL) (Figure 4a2–a4 and VOC 2007 test + local dataset graph in Figure 5) could increase the area and average precision of the precision–recall curve over time
In Figure 4a5, the fire-extinguisher class was well separated from the existing augmented class 9, and the hog class was different from the existing augmented class
Summary
There have been many advances in object detection technology since the deep-learning breakthrough achieved by Krizhevsky et al in 2012 [1]. The proposed method employs a dynamic hierarchical structure, each node of which keeps track of related data, features, and the deep model, and evolves itself in accordance with changing data distributions It adopts outlier detection for open-set active semi-supervised learning. Our method combines the incremental open-set aware active semi-supervised learning (ASSL) [16] and the dynamic hierarchical feature model (HFM) update algorithm for effectively grouping unseen objects together. Static world assumptions adopted by most detection methods [5,6,7,8,9,10] are no longer valid in practice We tackled this problem by leveraging the discriminative capability of the dynamic HFM embedded by the outlier detection algorithm and the collaborative sample selection-based open-set ASSL. We propose efficient open-set object detection using a flexible hierarchical structure that provides informative and nonredundant sample selection and the open-set-aware ASSL algorithm
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