Open World Object Detection (OWOD), simulating the real dynamic world where
knowledge grows continuously, attempts to detect both known and unknown classes
and incrementally learn the identified unknown ones. We find that although the
only previous OWOD work constructively puts forward to the OWOD definition, the
experimental settings are unreasonable with the illogical benchmark, confusing
metric calculation, and inappropriate method. In this paper, we rethink the
OWOD experimental setting and propose five fundamental benchmark principles to
guide the OWOD benchmark construction. Moreover, we design two fair evaluation
protocols specific to the OWOD problem, filling the void of evaluating from the
perspective of unknown classes. Furthermore, we introduce a novel and effective
OWOD framework containing an auxiliary Proposal ADvisor (PAD) and a
Class-specific Expelling Classifier (CEC). The non-parametric PAD could assist
the RPN in identifying accurate unknown proposals without supervision, while
CEC calibrates the over-confident activation boundary and filters out confusing
predictions through a class-specific expelling function. Comprehensive
experiments conducted on our fair benchmark demonstrate that our method
outperforms other state-of-the-art object detection approaches in terms of both
existing and our new metrics. Our benchmark and code are available at
this https URL

本文提出了基于开放世界目标检测（Open World Object Detection）的实验设置和基准原则，设计了两个公平的 OWOD 问题特定的评估协议，推出了一个包含辅助 Proposal ADvisor（PAD）和类别特定排除分类器（CEC）两部分的新型有效的 OWOD 框架，在公平的 OWOD 基准测试中，取得了优于其他现有物体检测方法的表现和新的度量方法。