Abstract
Facial Landmark Detection (FLD) in thermal imagery is critical for applications in challenging lighting conditions, but it is hampered by the lack of rich visual cues. Conventional cross-modal solutions, like feature fusion or image translation from RGB data, are often computationally expensive or introduce structural artifacts, limiting their practical deployment. To address this, we propose Multi-Level Cross-Modal Knowledge Distillation (MLCM-KD), a novel framework that decouples high-fidelity RGB-to-thermal knowledge transfer from model compression to create both accurate and efficient thermal FLD models. A central challenge during knowledge transfer is the profound modality gap between RGB and thermal data, where traditional unidirectional distillation fails to enforce semantic consistency across disparate feature spaces. To overcome this, we introduce Dual-Injected Knowledge Distillation (DIKD), a bidirectional mechanism designed specifically for this task. DIKD establishes a connection between modalities: it not only guides the thermal student with rich RGB features but also validates the student's learned representations by feeding them back into the frozen teacher's prediction head. This closed-loop supervision forces the student to learn modality-invariant features that are semantically aligned with the teacher, ensuring a robust and profound knowledge transfer. Experiments show that our approach sets a new state-of-the-art on public thermal FLD benchmarks, notably outperforming previous methods while drastically reducing computational overhead.
Abstract (translated)
在热成像中的面部特征点检测（FLD）对于应对挑战性光照条件的应用至关重要，但缺乏丰富的视觉线索阻碍了其发展。传统跨模态解决方案，如特征融合或从RGB数据转换图像，在计算成本高昂或引入结构化伪影方面往往限制了它们的实际部署。为解决这些问题，我们提出了一种名为多级跨模式知识蒸馏（MLCM-KD）的新型框架，该框架将高保真度的RGB到热成像的知识转移与模型压缩解耦，从而创建出既准确又高效的热成像FLD模型。 在知识转移过程中，RGB数据和热图像之间存在显著的模态差距，传统的单向蒸馏无法保证不同特征空间间的语义一致性。为此，我们引入了双注入知识蒸馏（DIKD），这是一种专门为此任务设计的双向机制。DIKD建立了模态之间的连接：它不仅通过丰富的RGB特性指导热成像学生模型，还通过将学生的表示反馈到冻结教师模型的预测头部来验证学生所学表示的有效性。这种闭环监督迫使学生学习出在语义上与老师一致且不受模态影响的特征，确保了深度和稳健的知识传递。 实验结果表明，我们提出的方法在公开热成像FLD基准测试中创下了新的性能记录，并显著优于先前方法，同时大幅减少了计算开销。
URL
https://arxiv.org/abs/2510.11128
