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。

无偏的新类别发现和定位

Debiased Novel Category Discovering and Localization

Changing an attribute of a text without changing the content usually requires
to first disentangle the text into irrelevant attributes and content
representations. After that, in the inference phase, the representation of one
attribute is tuned to a different value, expecting that the corresponding
attribute of the text can also be changed accordingly. The usual way of
disentanglement is to add some constraints on the latent space of an
encoder-decoder architecture, including adversarial-based constraints and
mutual-information-based constraints. However, the previous semi-supervised
processes of attribute change are usually not enough to guarantee the success
of attribute change and content preservation. In this paper, we propose a novel
approach to achieve a robust control of attributes while enhancing content
preservation. In this approach, we use a semi-supervised contrastive learning
method to encourage the disentanglement of attributes in latent spaces.
Differently from previous works, we re-disentangle the reconstructed sentence
and compare the re-disentangled latent space with the original latent space,
which makes a closed-loop disentanglement process. This also helps content
preservation. In addition, the contrastive learning method is also able to
replace the role of minimizing mutual information and adversarial training in
the disentanglement process, which alleviates the computation cost. We
conducted experiments on three text datasets, including the Yelp Service review
dataset, the Amazon Product review dataset, and the GoEmotions dataset. The
experimental results show the effectiveness of our model.

利用半监督对比学习方法，我们提出一种新的方法来实现属性的稳健控制，并增强内容的保留。通过重新解缠重构的句子并将其与原始潜变空间进行比较，我们实现了一个闭环解缠过程，进一步帮助内容保留。与以往方法不同，对比学习方法能够代替最小化互信息和对抗训练在解缠过程中的作用，从而减少计算成本。我们在三个文本数据集上进行了实验证明了我们模型的有效性。