Justin Kay

I am the Co-Founder and CTO of Ai.Fish. We work on computer vision and machine learning applications in sustainable fisheries management and marine conservation.

I am also a researcher in the Computatational Vision Lab at Caltech, where I work with Pietro Perona, Sara Beery, and Grant Van Horn on computer vision methods for biodiversity monitoring.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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I am interested in studying fundamental problems in computer vision and machine learning through the lens of real-world applications in the natural sciences, with a particular focus on conservation, sustainability, and climate.

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The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

Justin Kay, Peter Kulits, Suzanne Stathatos, Siqi Deng, Erik Young, Sara Beery, Grant Van Horn, and Pietro Perona
European Conference on Computer Vision (ECCV), 2022
website / arxiv / poster / slides /

A large-scale dataset for detecting, tracking, and counting fish in sonar videos, an important conservation application and a rich data source for advancing low signal-to-noise computer vision applications and tackling domain generalization in tracking and counting.

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Fine-Grained Counting with Crowd-Sourced Supervision

Justin Kay, Catherine M. Foley, and Tom Hart
Computer Vision for Animal Behavior Tracking and Modeling Workshop at CVPR, 2022
website / arxiv / poster /

A dataset and method for fine-grained (i.e. multiclass) seal counting using noisy volunteer dot annotations collected from the Zooniverse citizen science platform.

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The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries

Justin Kay and Matt Merrifield
8th Workshop on Fine-Grained Visual Categorization at CVPR, 2021
website / arxiv / poster /

A large and diverse image dataset sourced from fisheries electronic monitoring. Evaluation of existing detection and classification algorithms and quantification of key challenges.

Design and source code from here.