The ability to re-identify animals yields population estimates which are used in a variety of ecological metrics including: diversity, relative abundance distribution, and carrying capacity. These metrics contribute to larger, overarching ecological interpretations of trophic interactions and population dynamics. Monitoring these ecological metrics is impertative to ensure the stability of ecosystems globally. Current methods include physically tagging animals and DNA analyses, which are invasive, expensive, unreliable, and labourious. Our motivation is to bring together the ecological and computer vision communities to present and discuss deep learning methods for animal re-identification. This will be a forum for any researchers interested in presenting and learning more from within the wide spectrum of machine learning and ecology. We will host talks focused on ecological applications of machine learning and computer vision. We plan to hear from both CVML researchers and members of the ecological community, formulating problem statements and brainstorming how deep learning may aid their research efforts. Our expectation is that this is just the beginning of a major trend that could stand to revolutionize the analysis of camera trap data and, ultimately, our approach to animal ecology.
This workshop will facilitate the formation of interdisciplinary connections and collaborations between the ecological and computer vision communities. We will emphasize the accessibility of deep learning research and how it can make an immediate impact in ecological research efforts, as well as discussing the challenges of real-world data. We will discuss the growing need for interpretability and generalization in machine learning models for re-identification, and for ecological applications in general, and encourage the community to share best practices for data collection, curation, and management.
Researchers will come together to discuss how to push the bounds of ecological research using machine learning and computer vision for animal re-identification. Our expectation is that this workshop will expand the realm of problems machine learning is currently being used to solve within an ecological context, while starting the discussion between researchers focusing on different data sources (images, audio, or video). We plan to maintain a workshop webpage after the conference with a list of accepted work, and will use the pre-existing AI for Conservation slack channel to facilitate continued discussion amongst the participants.