Two-Level Residual Distillation based Triple Network for Incremental Object Detection

Understand

Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data.

• Due to time consumption, storage burden and privacy of old data, it is inadvisable to train the model from scratch with both old and new data when new object classes emerge after the model trained.
• In this paper, we propose a novel incremental object detector based on Faster R-CNN to continuously learn from new object classes without using old data.
• It is a triple network where an old model and a residual model as assistants for helping the incremental model learning on new classes without forgetting the previous learned knowledge.