This study explores semantic style transfer to enhance animal facial landmark detection, focusing on cat faces as a case study. The researchers demonstrate that cropping facial images before applying style transfer significantly improves the quality and structural consistency of generated images. Furthermore, while training models solely on style-transferred images can degrade performance, Supervised Style Transfer (SST), which selects style sources based on landmark accuracy, effectively mitigates this issue. Ultimately, augmenting existing datasets with these carefully curated, style-transferred images proves to be a powerful data augmentation strategy, outperforming traditional methods and improving the robustness and accuracy of facial landmark detection models for animals.
Non-Invasive Computer Vision-Based Fruit Fly Larvae Differentiation: Ceratitis capitata and Bactrocera zonata
This paper proposes a novel, non-invasive method using computer vision
