MammalNet: A Large-scale Video Benchmark for Mammal Recognition and
  Behavior Understanding
Jun Chen Ming Hu Darren J. Coker Michael L. Berumen Blair Costelloe Sara Beery Anna Rohrbach Mohamed Elhoseiny
Monitoring animal behavior can facilitate conservation efforts by providing
key insights into wildlife health, population status, and ecosystem function.
Automatic recognition of animals and their behaviors is critical for
capitalizing on the large unlabeled datasets generated by modern video devices
and for accelerating monitoring efforts at scale. However, the development of
automated recognition systems is currently hindered by a lack of appropriately
labeled datasets. Existing video datasets 1) do not classify animals according
to established biological taxonomies; 2) are too small to facilitate
large-scale behavioral studies and are often limited to a single species; and
3) do not feature temporally localized annotations and therefore do not
facilitate localization of targeted behaviors within longer video sequences.
Thus, we propose MammalNet, a new large-scale animal behavior dataset with
taxonomy-guided annotations of mammals and their common behaviors. MammalNet
contains over 18K videos totaling 539 hours, which is ~10 times larger than the
largest existing animal behavior dataset. It covers 17 orders, 69 families, and
173 mammal categories for animal categorization and captures 12 high-level
animal behaviors that received focus in previous animal behavior studies. We
establish three benchmarks on MammalNet: standard animal and behavior
recognition, compositional low-shot animal and behavior recognition, and
behavior detection. Our dataset and code have been made available at:
https://mammal-net.github.io.