  In real applications, new object classes often emerge after the detection
model has been trained on a prepared dataset with fixed classes. Due to the
storage burden and the privacy of old data, sometimes it is impractical to
train the model from scratch with both old and new data. Fine-tuning the old
model with only new data will lead to a well-known phenomenon of catastrophic
forgetting, which severely degrades the performance of modern object detectors.
In this paper, we propose a novel \textbf{M}ulti-\textbf{V}iew
\textbf{C}orrelation \textbf{D}istillation (MVCD) based incremental object
detection method, which explores the correlations in the feature space of the
two-stage object detector (Faster R-CNN). To better transfer the knowledge
learned from the old classes and maintain the ability to learn new classes, we
design correlation distillation losses from channel-wise, point-wise and
instance-wise views to regularize the learning of the incremental model. A new
metric named Stability-Plasticity-mAP is proposed to better evaluate both the
stability for old classes and the plasticity for new classes in incremental
object detection. The extensive experiments conducted on VOC2007 and COCO
demonstrate that MVCD can effectively learn to detect objects of new classes
and mitigate the problem of catastrophic forgetting.
