In this paper, we focus on training an open-set object detector under the
condition of scarce training samples, which should distinguish the known and
unknown categories. Under this challenging scenario, the decision boundaries of
unknowns are difficult to learn and often ambiguous. To mitigate this issue, we
develop a novel open-set object detection framework, which delves into
conditional evidence decoupling for the unknown rejection. Specifically, we
select pseudo-unknown samples by leveraging the discrepancy in attribution
gradients between known and unknown classes, alleviating the inadequate unknown
distribution coverage of training data. Subsequently, we propose a Conditional
Evidence Decoupling Loss (CEDL) based on Evidential Deep Learning (EDL) theory,
which decouples known and unknown properties in pseudo-unknown samples to learn
distinct knowledge, enhancing separability between knowns and unknowns.
Additionally, we propose an Abnormality Calibration Loss (ACL), which serves as
a regularization term to adjust the output probability distribution,
establishing robust decision boundaries for the unknown rejection. Our method
has achieved the superiority performance over previous state-of-the-art
approaches, improving the mean recall of unknown class by 7.24% across all
shots in VOC10-5-5 dataset settings and 1.38% in VOC-COCO dataset settings. The
code is available via this https URL

在稀缺训练样本条件下，我们通过开放集目标检测框架，采用条件证据解耦来区分已知和未知类别，并通过异常度校准损失来调整输出概率分布，提高未知拒绝的决策边界。在 VOC10-5-5 数据集和 VOC-COCO 数据集设置中，我们的方法表现优于之前的最先进方法，未知类别的平均召回率分别提高了 7.24% 和 1.38%。

揭示未知：未知拒绝的条件证据解耦

Unveiling the Unknown: Conditional Evidence Decoupling for Unknown  Rejection

Machine learning has achieved remarkable success in many applications.
However, existing studies are largely based on the closed-world assumption,
which assumes that the environment is stationary, and the model is fixed once
deployed. In many real-world applications, this fundamental and rather naive
assumption may not hold because an open environment is complex, dynamic, and
full of unknowns. In such cases, rejecting unknowns, discovering novelties, and
then incrementally learning them, could enable models to be safe and evolve
continually as biological systems do. This paper provides a holistic view of
open-world machine learning by investigating unknown rejection, novel class
discovery, and class-incremental learning in a unified paradigm. The
challenges, principles, and limitations of current methodologies are discussed
in detail. Finally, we discuss several potential directions for future
research. This paper aims to provide a comprehensive introduction to the
emerging open-world machine learning paradigm, to help researchers build more
powerful AI systems in their respective fields, and to promote the development
of artificial general intelligence.

综合研究开放环境下机器学习的未知拒绝、新类别发现和类别自适应学习等方面，探讨了当前方法的挑战、原则和限制，以及未来研究的潜在方向。旨在全面介绍新兴的开放环境机器学习范式，帮助研究人员在各自领域构建更强大的人工智能系统，并促进人工通用智能的发展。