  Multispectral pedestrian detection is attractive for around-the-clock
applications due to the complementary information between RGB and thermal
modalities. However, current models often fail to detect pedestrians in certain
cases (e.g., thermal-obscured pedestrians), particularly due to the modality
bias learned from statistically biased datasets. In this paper, we investigate
how to mitigate modality bias in multispectral pedestrian detection using Large
Language Models (LLMs). Accordingly, we design a Multispectral Chain-of-Thought
(MSCoT) prompting strategy, which prompts the LLM to perform multispectral
pedestrian detection. Moreover, we propose a novel Multispectral
Chain-of-Thought Detection (MSCoTDet) framework that integrates MSCoT prompting
into multispectral pedestrian detection. To this end, we design a
Language-driven Multi-modal Fusion (LMF) strategy that enables fusing the
outputs of MSCoT prompting with the detection results of vision-based
multispectral pedestrian detection models. Extensive experiments validate that
MSCoTDet effectively mitigates modality biases and improves multispectral
pedestrian detection.
