DIODE: Dilatable Incremental Object Detection
release_v7drdcnu65f5lg7vjjgvz5i7sm
by
Can Peng, Kun Zhao, Sam Maksoud, Tianren Wang, Brian C. Lovell
2021
Abstract
To accommodate rapid changes in the real world, the cognition system of
humans is capable of continually learning concepts. On the contrary,
conventional deep learning models lack this capability of preserving previously
learned knowledge. When a neural network is fine-tuned to learn new tasks, its
performance on previously trained tasks will significantly deteriorate. Many
recent works on incremental object detection tackle this problem by introducing
advanced regularization. Although these methods have shown promising results,
the benefits are often short-lived after the first incremental step. Under
multi-step incremental learning, the trade-off between old knowledge preserving
and new task learning becomes progressively more severe. Thus, the performance
of regularization-based incremental object detectors gradually decays for
subsequent learning steps. In this paper, we aim to alleviate this performance
decay on multi-step incremental detection tasks by proposing a dilatable
incremental object detector (DIODE). For the task-shared parameters, our method
adaptively penalizes the changes of important weights for previous tasks. At
the same time, the structure of the model is dilated or expanded by a limited
number of task-specific parameters to promote new task learning. Extensive
experiments on PASCAL VOC and COCO datasets demonstrate substantial
improvements over the state-of-the-art methods. Notably, compared with the
state-of-the-art methods, our method achieves up to 6.0% performance
improvement by increasing the number of parameters by just 1.2% for each newly
learned task.
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