发表机构
Zhongnan University of Economics and Law（中南财经政法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出FAHCD-Net，通过频率自适应热图条件扩散模型与平滑正则化损失级联，抑制高频噪声并增强热图平滑性，在挑战性场景下实现人脸关键点检测的最先进性能。
AI 中文摘要
人脸关键点检测（FLD）是各种应用中的一项关键任务，近年来已取得显著进展。然而，当前FLD方法在挑战性条件下仍面临困难，其中面部结构变化、信息丢失和噪声干扰严重损害了学习到的面部特征的完整性和准确性。为解决这些问题，我们提出了频率自适应热图条件扩散网络（FAHCD-Net），它在级联框架中集成了频率自适应热图条件扩散（FAHCD）模型与平滑正则化（SR）损失。具体而言，FAHCD模型包含一个层次频率自适应（HFA）模块，该模块通过多层频率分解和自适应重构来抑制冗余的高频噪声，从而保留关键的面部结构。此外，提出SR损失以进一步减轻高频噪声的干扰并增强生成的关键点热图的平滑性。通过将FAHCD模型与SR损失级联，FAHCD-Net有效利用数据的统计和基于频率的分布特征，从噪声输入逐步生成更准确的关键点热图。在流行基准上的大量实验证明了所提方法的有效性和鲁棒性，在挑战性场景下的FLD任务中达到了最先进的性能。源代码可在以下网址获取：this https URL。
英文摘要
Facial Landmark Detection(FLD) is a crucial task in various applications and has achieved significant advancements in recent years. However, current FLD methods still struggle under challenging conditions, where facial structural variations, information loss, and noise interference severely compromise the integrity and accuracy of learned facial features. To address these issues, we propose Frequency-Adaptive Heatmap-Conditional Diffusion Network (FAHCD-Net), which integrates a Frequency-Adaptive Heatmap-Conditional Diffusion (FAHCD) model with a Smoothness Regularization (SR) loss in a cascaded framework. Specifically, the FAHCD model incorporates a Hierarchical Frequency Adaptation (HFA) module designed to suppress redundant high-frequency noise through multi-layer frequency decomposition and adaptive reconstruction, thereby preserving essential facial structures. Additionally, the SR loss is proposed to further mitigate the interference of high-frequency noise and enhance the smoothness of the generated landmark heatmaps. By cascading the FAHCD model with the SR loss, FAHCD-Net effectively leverages both statistical and frequency-based distribution characteristics of the data to progressively generate more accurate landmark heatmaps from noisy inputs. Extensive experiments on popular benchmarks demonstrate the effectiveness and robustness of the proposed method, achieving state-of-the-art performance in FLD tasks under challenging scenarios. The source code is available at https://github.com/HJWKryptonite/FAHCD-Net.
