Animal Specie

Research on animal species is increasingly leveraging computer vision and machine learning to address challenges in wildlife monitoring and understanding animal behavior. Current efforts focus on developing robust models for animal re-identification, pose estimation, and behavior classification using various deep learning architectures, including convolutional neural networks (CNNs) and transformer-based models, often incorporating techniques like contrastive learning and hierarchical classification. These advancements facilitate large-scale data analysis from sources such as camera traps and acoustic recordings, improving biodiversity monitoring, conservation efforts, and our understanding of animal ecology and social interactions. The development of large, well-annotated datasets is crucial for training these models and ensuring their generalizability across diverse species and environments.