Lifelong Object Detection

Understand

Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.

• However, neural networks suffer from the well-known issue of catastrophic forgetting, which makes continual or lifelong learning problematic.
• In this paper, we leverage the fact that new training classes arrive in a sequential manner and incrementally refine the model so that it additionally detects new object classes in the absence of previous training data.
• Specifically, we consider the representative object detector, Faster R-CNN, for both accurate and efficient prediction.