Incremental Object Detection via Meta-Learning
This addresses the issue of performance deterioration on old classes when object detectors encounter new classes incrementally, which is a critical requirement for real-world applications.
The paper tackles the problem of catastrophic forgetting in incremental object detection by proposing a meta-learning approach that reshapes model gradients to minimize forgetting and maximize knowledge transfer. The method achieves favorable performance against state-of-the-art methods on PASCAL-VOC and MS COCO datasets.
In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods.