Building robust biodiversity-focused models for passive monitoring sensors

Ecological data is frequently collected from static sensors, like camera traps, acoustic receivers like AudioMoth, or static sonar used to monitor species underwater. This data presents challenges that are not well addressed by existing machine learning methods, including a large amount of "empty" data, a small number of examples for most species, strong and often spurious correlations across data collected from one sensor installation, and highly variable signal quality. In this tutorial, we will discuss some of the ways to adapt existing methods to handle these challenges and get hands-on with a real-world dataset to determine how to best structure the data for training and evaluation of ML methods.

Talk is based on the speakers' papers:

WILDS: A Benchmark of in-the-Wild Distribution Shifts. (ICML 2021).
Arxiv: https://arxiv.org/abs/2012.07421
The iWildCam 2021 Competition Dataset. (FGVC8 @ CVPR 2021).
Arxiv: https://arxiv.org/abs/2105.03494
Git: https://github.com/visipedia/iwildcam_comp
Kaggle competition: https://www.kaggle.com/c/iwildcam2021-fgvc8

The iWildCam 2020 Competition Dataset (FGVC7 @ CVPR 2020).
Arxiv: https://arxiv.org/abs/2004.10340
Git: https://github.com/visipedia/iwildcam_comp
Kaggle competition: https://www.kaggle.com/c/iwildcam-2020-fgvc7

Automated Salmonid Counting in Sonar Data (CCAI @ NeurIPS 2020).
Paper: https://www.climatechange.ai/papers/neurips2020/54.html

Context R-CNN: Long term temporal context for per-camera object detection (CVPR 2020)
Arxiv: https://arxiv.org/abs/1912.03538

Synthetic examples improve generalization for rare classes (WACV 2020)
Arxiv: https://arxiv.org/abs/1904.05916

Recognition in terra incognita (ECCV 2018)
Arxiv: https://arxiv.org/abs/1807.04975

Presenter BIO:

Sara Beery has always been passionate about the natural world, and she saw a need for technology-based approaches to conservation and sustainability challenges. This led her to pursue a PhD at Caltech advised by Pietro Perona, where her research focuses on computer vision for global-scale biodiversity monitoring. Her work is funded by an NSF Graduate Research Fellowship, a PIMCO Data Science Fellowship, and an Amazon AI4Science Fellowship. She works closely with Microsoft AI for Earth and Google Research to translate her work into accessible, usable tools for the ecological community. Sara’s prior experience as a professional ballerina and a nontraditional student has taught her the value of unique and diverse perspectives in the research community. She’s passionate about increasing diversity and inclusion in STEM through mentorship, teaching, and outreach.

Sara's homepage: https://beerys.github.io/

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