Portrait de Juan Sebastián Cañas

Juan Sebastián Cañas

Collaborateur·rice de recherche
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Apprentissage profond
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Insect Identification in the Wild: The AMI Dataset
Aditya Jain
Fagner Cunha
M. J. Bunsen
Juan Sebastián Cañas
L. Pasi
N. Pinoy
Flemming Helsing
JoAnne Russo
Marc Botham
Michael Sabourin
Jonathan Fréchette
Alexandre Anctil
Yacksecari Lopez
Eduardo Navarro
Filonila Perez Pimentel
Ana Cecilia Zamora
José Alejandro Ramirez Silva
Jonathan Gagnon
Tom August
K. Bjerge … (voir 8 de plus)
Alba Gomez Segura
Marc Bélisle
Yves Basset
K. P. McFarland
David Roy
Toke Thomas Høye
Maxim Larrivée
David Rolnick
Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems… (voir plus) and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups.
2024-12-02
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Insect Identification in the Wild: The AMI Dataset
Aditya Jain
Fagner Cunha
M. Bunsen
Juan Sebasti'an Canas
L. Pasi
N. Pinoy
Flemming Helsing
JoAnne Russo
Marc Botham
Michael Sabourin
Jonathan Fr'echette
Alexandre Anctil
Yacksecari Lopez
Eduardo Navarro
Filonila Perez Pimentel
Ana Cecilia Zamora
José Alejandro Ramirez Silva
Jonathan Gagnon
T. August
Kim Bjerge … (voir 8 de plus)
Alba Gomez Segura
Marc B'elisle
Yves Basset
K. P. McFarland
David Roy
Toke Thomas Høye
Maxim Larriv'ee
David Rolnick
Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems… (voir plus) and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups.
2024-06-18
ArXiv (prépublication)
doi.org
arxiv.org