An active learning model to classify animal species in Hong Kong
<|reference_start|>An active learning model to classify animal species in Hong Kong: Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong.<|reference_end|>
@article{lamb2024an,
    title={An active learning model to classify animal species in Hong Kong},
    author={Gareth Lamb (1), Ching Hei Lo (1), Jin Wu (1), Calvin K. F. Lee (1)
  ((1) School of Biological Sciences, University of Hong Kong)},
    journal={arXiv preprint arXiv:2403.15675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15675},
    primaryClass={cs.CV}
}