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.

翻译：开放世界目标检测（OWOD）是一个结合了目标检测、增量学习与开集学习的挑战性问题。相较于标准目标检测，OWOD设置的任务要求：1）在训练过程中检测已见类别的同时识别未见类别，2）在获得对应标注时增量式学习已识别未知物体的知识。我们提出了一种新颖且高效的基于原型视角的OWOD解决方案，命名为OCPL：基于判别性类别原型学习的开放世界目标检测。该方法由提案嵌入聚合器（PEA）、嵌入空间压缩器（ESC）和基于余弦相似度的分类器（CSC）三个模块组成。所有提出的模块旨在学习特征空间中已知类别的判别性嵌入，以最小化已知与未知类别的分布重叠，从而有利于区分已知与未知类别。在PASCAL VOC和MS-COCO基准数据集上进行的大量实验证明了我们提出方法的有效性。