In recent years, object detection in deep learning has experienced rapid
development. However, most existing object detection models perform well only
on closed-set datasets, ignoring a large number of potential objects whose
categories are not defined in the training set. These objects are often
identified as background or incorrectly classified as pre-defined categories by
the detectors. In this paper, we focus on the challenging problem of Novel
Class Discovery and Localization (NCDL), aiming to train detectors that can
detect the categories present in the training data, while also actively
discover, localize, and cluster new categories. We analyze existing NCDL
methods and identify the core issue: object detectors tend to be biased towards
seen objects, and this leads to the neglect of unseen targets. To address this
issue, we first propose an Debiased Region Mining (DRM) approach that combines
class-agnostic Region Proposal Network (RPN) and class-aware RPN in a
complementary manner. Additionally, we suggest to improve the representation
network through semi-supervised contrastive learning by leveraging unlabeled
data. Finally, we adopt a simple and efficient mini-batch K-means clustering
method for novel class discovery. We conduct extensive experiments on the NCDL
benchmark, and the results demonstrate that the proposed DRM approach
significantly outperforms previous methods, establishing a new
state-of-the-art.

通过 Debiased Region Mining (DRM) 方法和半监督对比学习来解决目标检测中的偏见问题，提出了一种能够有效发现、定位和聚类新类别的训练器，其在 NCDL 基准测试中显著优于先前的方法，成为了新的最先进技术。