A hybrid approach that combines saliency from multiple sources (human subjects, models, and segmentation models) has the potential to enhance generalization performance further. By leveraging the strengths of each source, this approach can mitigate the limitations of individual sources and provide a more comprehensive and diverse set of saliency information. Human subjects can offer nuanced and contextually relevant saliency annotations, capturing subtle features that may be challenging for models to replicate. Models trained to mimic human saliency can provide scalability and efficiency, generating saliency maps at scale. Domain-specific segmentation models can contribute additional feature-level masks, offering a different perspective on salient regions. By integrating saliency from multiple sources, the hybrid approach can create a more robust and comprehensive training dataset, potentially leading to improved model generalization across different biometric modalities. However, careful consideration should be given to the integration process to ensure that the combined saliency information is complementary and does not introduce conflicting signals during training.