Pseudo Object Replay and Mining for Incremental Object Detection

Incremental object detection (IOD) aims to mitigate catastrophic forgetting for object detectors when incrementally learning to detect new emerging object classes without using original training data. Most existing IOD methods benefit from the assumption that unlabeled old-class objects may co-occur with labeled new-class objects in the new training data. However, in practical scenarios, old-class objects may be absent, which is called non co-occurrence IOD. In this paper, we propose a pseudo object replay and mining method (PseudoRM) to handle the co-occurrence dependent problem, reducing the performance degradation caused by the absence of old-class objects. The new training data can be augmented by co-occurring fake (old-class) and real (new-class) objects with a patch-level data-free generation method in the pseudo object replay stage. To fully use existing training data, we propose pseudo object mining to explore false positives for transferring useful instance-level knowledge. In the incremental learning procedure, a generative distillation is introduced to distill image-level knowledge for balancing stability and plasticity. Experimental results on PASCAL VOC and COCO demonstrate that PseudoRM can effectively boost the performance on both co-occurrence and non co-occurrence scenarios without using old samples or extra wild data.