Efficient Incremental Learning for Mobile Object Detection

by   Dawei Li, et al.
University of Southern California

Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.


Multi-View Correlation Distillation for Incremental Object Detection

In real applications, new object classes often emerge after the detectio...

Incremental Learning of Object Detectors without Catastrophic Forgetting

Despite their success for object detection, convolutional neural network...

Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection

Deep learning-based approaches have shown remarkable performance in the ...

Towards Class-incremental Object Detection with Nearest Mean of Exemplars

Object detection has been widely used in the field of Internet, and deep...

Scalable Deep Learning Logo Detection

Existing logo detection methods usually consider a small number of logo ...

Cross-dataset Training for Class Increasing Object Detection

We present a conceptually simple, flexible and general framework for cro...

Web-Scale Generic Object Detection at Microsoft Bing

In this paper, we present Generic Object Detection (GenOD), one of the l...

Please sign up or login with your details

Forgot password? Click here to reset