RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically, datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation, such as ROTX data. To address this problem, we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover, we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST, CVC-14, FLIR) and the new ROTX-MP. We will release our new dataset to the public for future research.

为了解决RGBT多光谱行人检测中模态偏差问题，文中提出了一种新的因果模式复用器（Causal Mode Multiplexer）框架，并构建了新的ROTX-MP数据集用于评估模态偏差。通过大量实验证明，所提出的CMM框架在现有数据集（KAIST、CVC-14、FLIR）和新的ROTX-MP数据集上具有很好的泛化能力，并将该数据集公开发布用于未来的研究。

因果模态复用器：一种用于无偏多光谱行人检测的新型框架