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Learning Causal Features for Incremental Object Detection

2024-03-01 15:14:43
Zhenwei He, Lei Zhang

Abstract

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.

Abstract (translated)

在训练阶段,对象的检测限制了其可识别的类别,用户无法覆盖所有感兴趣的对象。为了满足实际需求,检测器的递增学习能力成为现实应用的关键因素。然而,当在新技术上实现神经网络时,它不可避免地遇到灾难性遗忘问题。为了应对这一挑战,许多递增物体检测模型通过从以前的模型中回放样本或从以前的模型中进行蒸馏来保留以前任务的记忆。然而,它们忽视了一个重要因素,即模型的性能主要取决于其特征。这些模型试图通过以前样本的内存唤醒来激发神经网络的记忆,而不是防止遗忘。因此,本文提出了一种递增因果物体检测(ICOD)模型,通过学习因果特征来适应更多任务。传统的物体检测模型不可避免地依赖于数据偏差或特定数据特征来获得检测结果,不能适应新任务。当模型满足递增学习要求时,数据偏差信息对新的任务没有帮助,递增学习可能导致遗忘。因此,我们在训练检测器时引入了ICOD,以学习因果特征,而不是数据偏差特征。这样,当模型应用于新任务时,旧任务的因果特征可以协助递增学习过程解决灾难性遗忘问题。我们对我们的模型在多个实验中进行了研究,结果表明,没有数据偏见的因果特征可以使模型更好地适应新任务。关键词{物体检测,递增学习,因果特征。

URL

https://arxiv.org/abs/2403.00591

PDF

https://arxiv.org/pdf/2403.00591.pdf


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