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:开放世界天体探测(OWOD)是一个具有挑战性的问题,将物体探测与渐进式学习和开放式学习结合起来。与标准天体探测相比,OWOD设置的任务是:1)探测培训期间看到的物体,同时识别不可见的班级,2)在相应的说明可用时逐步学习对已查明的未知物体的知识。我们从原型的角度提出了一个新颖和有效的OOOOD解决方案,我们称之为OCPL:通过歧视性的等级原型学习(包括一个嵌入式聚合器(PEA)、嵌入式空间压缩器(ESC)和基于气候相似性的分类(CSCC))来探测物体。我们所有拟议的模块都旨在学习在特性空间内已知的已知种类的歧视性嵌入,以尽量减少已知和未知类别之间的重叠分布,这有助于区分已知和未知的类别。在PACAL VOC和MS-CO基准上进行的广泛实验显示了我们拟议方法的有效性。