Crowd Counting has important applications in public safety and pandemic control. A robust and practical crowd counting system has to be capable of continuously learning with the new-coming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks to handle multiple domains. 1) The models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies of intrinsic data distributions from various domains, which is called catastrophic forgetting. 2) The well-trained model in a specific domain achieves imperfect performance among other unseen domains because of the domain shift. 3) It leads to linearly-increased storage overhead either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new task of crowd counting under the incremental domains training setting, namely, Lifelong Crowd Counting. It aims at alleviating the catastrophic forgetting and improving the generalization ability using a single model updated by the incremental domains. To be more specific, we propose a self-distillation learning framework as a benchmark~(Forget Less, Count Better, FLCB) for lifelong crowd counting, which helps the model sustainably leverage previous meaningful knowledge for better crowd counting to mitigate the forgetting when the new data arrive. Meanwhile, a new quantitative metric, normalized backward transfer~(nBwT), is developed to evaluate the forgetting degree of the model in the lifelong learning process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

本研究探讨的是一种新的人群计数任务——永生人群计数，以缓解新数据到达时所带来的遗忘现象和提高模型的泛化能力，特别地，我们提出了一个自蒸馏学习框架作为衡量标准，并开发了一个新的定量度量标准，用于评估模型在终身学习过程中的遗忘程度。大量的实验结果表明，我们提出的框架在实现低度灾难性遗忘和强大的泛化性能方面具有优越性。

遗忘少量、计数更准：面向终身群体计数的领域增量自蒸馏学习基准