Semantic Style Transfer for Enhancing Animal Facial Landmark Detection

2025-05-08

AI Summary

This work addresses performance bottlenecks in animal (particularly feline) facial landmark detection caused by domain shift and annotation noise. To this end, we propose a novel data augmentation paradigm based on semantic style transfer. Methodologically, we are the first to introduce semantic style transfer into animal facial landmark detection; we design Supervised Style Transfer (SST), which dynamically selects style source images based on landmark localization accuracy to jointly optimize stylistic diversity and geometric structure fidelity; and we further enhance robustness via facial region cropping and multi-model ensemble. Experiments demonstrate that SST preserves 98% of baseline accuracy while substantially outperforming conventional data augmentation techniques. Notably, it achieves marked improvements in cross-domain generalization and occlusion robustness. Moreover, SST exhibits strong transferability to other animal species and related landmark detection tasks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• Enhancing animal facial landmark detection using style transfer
• Improving structural consistency in style-transferred facial images
• Addressing annotation misalignment with supervised style transfer

Innovation

• Uses Neural Style Transfer for data augmentation
• Applies style transfer to cropped facial images
• Introduces Supervised Style Transfer for accuracy