通过多队列对比学习提升恶劣天气下的人群计数
Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning
AI总结：
本文提出多队列对比学习（MQCL）两阶段人群计数方法，通过天气感知表示学习与域转换缓解恶劣天气域差距和类别不平衡，在恶劣天气下将计数误差降低22%，计算负担仅增加约13%。
摘要：
目前，大多数人群计数方法在正常天气条件下具有出色的性能。然而，我们的实验验证揭示了限制人群计数模型精度提升的两个关键障碍：1）恶劣天气图像与正常天气图像之间的域差距；2）训练集中的天气类别不平衡。为了解决这些问题，我们提出了一种名为多队列对比学习（MQCL）的两阶段人群计数方法。具体而言，在第一阶段，我们的目标是使骨干网络具备天气感知能力。在此过程中，我们采用了一种由我们设计的名为多队列 MoCo 的对比学习方法，以实现在天气类别不平衡情况下的表示学习。第一阶段完成后，骨干模型已经足够“成熟”，能够提取与天气相关的表示。在此基础上，我们进入第二阶段，在该阶段中我们提出在对比学习的指导下细化表示，从而将天气感知表示转换到正常天气域。通过这种表示与转换，模型在正常和恶劣天气条件下均实现了稳健的计数性能。大量实验结果表明，与基线相比，MQCL 在恶劣天气条件下将计数误差降低了 22%，而计算负担仅增加约 13%，达到了最先进的性能。
英文摘要：
Currently, most crowd counting methods have outstanding performance under normal weather conditions. However, our experimental validation reveals two key obstacles limiting the accuracy improvement of crowd counting models: 1) the domain gap between the adverse weather and the normal weather images; 2) the weather class imbalance in the training set. To address the problems, we propose a two-stage crowd counting method named Multi-queue Contrastive Learning (MQCL). Specifically, in the first stage, our target is to equip the backbone network with weather-awareness capabilities. In this process, a contrastive learning method named multi-queue MoCo designed by us is employed to enable representation learning under weather class imbalance. After the first stage is completed, the backbone model is "mature" enough to extract weather-related representations. On this basis, we proceed to the second stage, in which we propose to refine the representations under the guidance of contrastive learning, enabling the conversion of the weather-aware representations to the normal weather domain. Through such representation and conversion, the model achieves robust counting performance under both normal and adverse weather conditions. Extensive experimental results show that, compared to the baseline, MQCL reduces the counting error under adverse weather conditions by 22%, while introducing only about 13% increase in computational burden, which achieves state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
