Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification (Papers Track) Spotlight

Marta Skreta (University of Toronto); Sasha Luccioni (Mila); David Rolnick (McGill University, Mila)

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Abstract

Understanding the changing distributions of butterflies gives insight into the impacts of climate change across ecosystems and is a prerequisite for conservation efforts. eButterfly is a citizen science website created to allow people to track the butterfly species around them and use these observations to contribute to research. However, correctly identifying butterfly species is a challenging task for non-specialists and currently requires the involvement of entomologists to verify the labels of novice users on the website. We have developed a computer vision model to label butterfly images from eButterfly automatically, decreasing the need for human experts. We employ a model that incorporates geographic and temporal information of where and when the image was taken, in addition to the image itself. We show that we can successfully apply this spatiotemporal model for fine-grained image recognition, significantly improving the accuracy of our classification model compared to a baseline image recognition system trained on the same dataset.

Recorded Talk (direct link)

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