A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved impressive performance on various datasets, they remain sensitive to shifts in the distribution of the inference data. Our method adopts and modifies Elastic Weight Consolidation to a backbone object detection network, in order to penalize the changes in the model weights based on their importance towards the initially learned task. We show that when trained with one dataset and fine-tuned on another, our solution learns the new distribution and maintains its performance on the previous one, avoiding catastrophic forgetting. We use two popular datasets, CrowdHuman and CityPersons for our cross-dataset experiments, and show considerable improvements over standard fine-tuning, with a 9% and 18% miss rate percent reduction improvement in the CrowdHuman and CityPersons datasets, respectively.

翻译：本文提出了一种持续学习解决方案，用于解决行人检测中的分布外泛化问题。尽管近期行人检测模型在各种数据集上取得了显著性能，但它们对推理数据分布的偏移仍然敏感。我们的方法采用并改进了弹性权重巩固（Elastic Weight Consolidation）技术，将其应用于主干目标检测网络，基于模型权重对初始学习任务的重要性来惩罚其变化。实验表明，当模型在一个数据集上训练并在另一个数据集上微调时，我们的解决方案能够学习新分布的同时保持对先前分布的性能，避免灾难性遗忘。我们使用CrowdHuman和CityPersons两个流行数据集进行跨数据集实验，结果显示与标准微调相比，我们的方法在CrowdHuman和CityPersons数据集上分别实现了9%和18%的漏检率降低改善。