Learning Causal Features for Incremental Object Detection
Zhenwei He Lei Zhang
Object detection limits its recognizable categories during the training
phase, in which it can not cover all objects of interest for users. To satisfy
the practical necessity, the incremental learning ability of the detector
becomes a critical factor for real-world applications. Unfortunately, neural
networks unavoidably meet catastrophic forgetting problem when it is
implemented on a new task. To this end, many incremental object detection
models preserve the knowledge of previous tasks by replaying samples or
distillation from previous models. However, they ignore an important factor
that the performance of the model mostly depends on its feature. These models
try to rouse the memory of the neural network with previous samples but not to
prevent forgetting. To this end, in this paper, we propose an incremental
causal object detection (ICOD) model by learning causal features, which can
adapt to more tasks. Traditional object detection models, unavoidably depend on
the data-bias or data-specific features to get the detection results, which can
not adapt to the new task. When the model meets the requirements of incremental
learning, the data-bias information is not beneficial to the new task, and the
incremental learning may eliminate these features and lead to forgetting. To
this end, our ICOD is introduced to learn the causal features, rather than the
data-bias features when training the detector. Thus, when the model is
implemented to a new task, the causal features of the old task can aid the
incremental learning process to alleviate the catastrophic forgetting problem.
We conduct our model on several experiments, which shows a causal feature
without data-bias can make the model adapt to new tasks better.
\keywords{Object detection, incremental learning, causal feature.