Open-world object detection (OWOD) is a challenging problem that combines
object detection with incremental learning and open-set learning. Compared to
standard object detection, the OWOD setting is task to: 1) detect objects seen
during training while identifying unseen classes, and 2) incrementally learn
the knowledge of the identified unknown objects when the corresponding
annotations is available. We propose a novel and efficient OWOD solution from a
prototype perspective, which we call OCPL: Open-world object detection via
discriminative Class Prototype Learning, which consists of a Proposal Embedding
Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine
Similarity-based Classifier (CSC). All our proposed modules aim to learn the
discriminative embeddings of known classes in the feature space to minimize the
overlapping distributions of known and unknown classes, which is beneficial to
differentiate known and unknown classes. Extensive experiments performed on
PASCAL VOC and MS-COCO benchmark demonstrate the effectiveness of our proposed
method.

基于原型学习的开放世界目标检测方法 OCPL，包括 PEA、ESC、CSC 三个模块，用于学习已知类别的鉴别性嵌入以区分已知和未知类别，相关实验结果验证了该方法的有效性。

通过判别类原型学习的开放世界物体检测

Open-World Object Detection via Discriminative Class Prototype Learning

Traditional semantic segmentation methods can recognize at test time only the
classes that are present in the training set. This is a significant limitation,
especially for semantic segmentation algorithms mounted on intelligent
autonomous systems, deployed in realistic settings. Regardless of how many
classes the system has seen at training time, it is inevitable that unexpected,
unknown objects will appear at test time. The failure in identifying such
anomalies may lead to incorrect, even dangerous behaviors of the autonomous
agent equipped with such segmentation model when deployed in the real world.
Current state of the art of anomaly segmentation uses generative models,
exploiting their incapability to reconstruct patterns unseen during training.
However, training these models is expensive, and their generated artifacts may
create false anomalies. In this paper we take a different route and we propose
to address anomaly segmentation through prototype learning. Our intuition is
that anomalous pixels are those that are dissimilar to all class prototypes
known by the model. We extract class prototypes from the training data in a
lightweight manner using a cosine similarity-based classifier. Experiments on
StreetHazards show that our approach achieves the new state of the art, with a
significant margin over previous works, despite the reduced computational
overhead. Code is available at this https URL

提出由余弦相似度分类器轻量化地从训练数据中提取类原型，并通过原型学习的方式解决语义分割中的异常检测问题，达到了具有显著优势的最新技术水平，可应用于智能自主系统中。