Animal Re Identification

Animal re-identification (Re-ID) focuses on automatically identifying individual animals from images or videos, primarily aiding wildlife conservation and research. Current research emphasizes developing robust models that address challenges like variations in pose, lighting, and background, often employing deep learning architectures such as transformers and convolutional neural networks, along with techniques like contrastive learning and triplet loss for effective feature extraction and embedding generation. The availability of large, diverse datasets, including those with time-aware splits to avoid performance overestimation, is crucial for advancing the field and enabling more accurate and reliable animal monitoring in various ecological contexts.