用于增强动物面部关键点检测的语义风格迁移
Semantic Style Transfer for Enhancing Animal Facial Landmark Detection
AI总结：
本文提出将语义风格迁移用于动物面部关键点检测训练，通过在裁剪面部图像上应用监督式风格迁移和数据增强，在猫面部关键点检测任务中保留98%基线精度并提升鲁棒性，且方法可推广至其他物种。
摘要：
神经风格迁移（Neural Style Transfer, NST）是一种将一幅图像的视觉特征应用到另一幅图像上、同时保留结构内容的技术。NST传统上用于艺术变换，近年来已被应用于领域自适应和数据增强等任务。本研究探讨了利用该技术增强动物面部关键点检测器训练的方法。作为案例研究，我们使用最近提出的用于48个猫面部解剖关键点的集成关键点检测器（Ensemble Landmark Detector）及其训练所用的CatFLW数据集，做出了三项主要贡献。首先，我们证明对裁剪后的面部图像而非全身图像应用风格迁移可以增强结构一致性，从而提高生成图像的质量。其次，用风格迁移后的图像替换训练图像带来了标注错位的问题，但监督式风格迁移（Supervised Style Transfer, SST）——根据关键点精度选择风格源——保留了高达98%的基线精度。最后，用风格迁移后的图像扩充数据集进一步提高了鲁棒性，优于传统的数据增强方法。这些发现确立了语义风格迁移作为一种有效的数据增强策略，可用于提升动物及其他领域面部关键点检测模型的性能。虽然本研究聚焦于猫面部关键点，但所提出的方法可以推广到其他物种和关键点检测模型。
英文摘要：
Neural Style Transfer (NST) is a technique for applying the visual characteristics of one image onto another while preserving structural content. Traditionally used for artistic transformations, NST has recently been adapted, e.g., for domain adaptation and data augmentation. This study investigates the use of this technique for enhancing animal facial landmark detectors training. As a case study, we use a recently introduced Ensemble Landmark Detector for 48 anatomical cat facial landmarks and the CatFLW dataset it was trained on, making three main contributions. First, we demonstrate that applying style transfer to cropped facial images rather than full-body images enhances structural consistency, improving the quality of generated images. Secondly, replacing training images with style-transferred versions raised challenges of annotation misalignment, but Supervised Style Transfer (SST) - which selects style sources based on landmark accuracy - retained up to 98% of baseline accuracy. Finally, augmenting the dataset with style-transferred images further improved robustness, outperforming traditional augmentation methods. These findings establish semantic style transfer as an effective augmentation strategy for enhancing the performance of facial landmark detection models for animals and beyond. While this study focuses on cat facial landmarks, the proposed method can be generalized to other species and landmark detection models.
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