13 citations · 21 across the 7 of their papers we have counts for
12 papers
CFC26: Building Evaluations for Deployment in Sonar-Based Fish Counting
Madison Van Horn, Suzanne Stathatos, Sevan Brodjian +6
Accurate counts of fish populations are essential for monitoring ecosystems and making decisions for conservation, harvest limits, and river management. In many field deployments,…
Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling
Timm Haucke, Lauren Harrell, Justin Kay +2
We increasingly use machine learning to label scientific datasets. The models we develop and deploy are improving all the time, but they are not and will likely never be perfect. M…
MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
Justin Kay, Shir Bar, Ellen O. Aikens +28
Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory forecasting has advanced for human and vehicle movement, wildlife trajecto…
Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance
Kai Van Brunt, Justin Kay, Sara Beery
Computer vision is increasingly used to automate recognition tasks in large ecological datasets, but more complex tasks such as multi-object tracking continue to pose challenges. A…
Consensus-Driven Active Model Selection
Justin Kay, Grant Van Horn, Subhransu Maji +2
The widespread availability of off-the-shelf machine learning models poses a challenge: which model, of the many available candidates, should be chosen for a given data analysis ta…
Counting Fish with Temporal Representations of Sonar Video
Kai Van Brunt, Justin Kay, Timm Haucke +3
Accurate estimates of salmon escapement - the number of fish migrating upstream to spawn - are key data for conservation and fishery management. Existing methods for salmon countin…
Comparing fine-grained and coarse-grained object detection for ecology
Jess Tam, Justin Kay
Computer vision applications are increasingly popular for wildlife monitoring tasks. While some studies focus on the monitoring of a single species, such as a particular endangered…
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
Justin Kay, Timm Haucke, Suzanne Stathatos +5
Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on add…
Teaching Computer Vision for Ecology
Elijah Cole, Suzanne Stathatos, Björn Lütjens +5
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites. However, computer vision is an eme…
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
Justin Kay, Peter Kulits, Suzanne Stathatos +5
We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of d…
Fine-Grained Counting with Crowd-Sourced Supervision
Justin Kay, Catherine M. Foley, Tom Hart
Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis f…
The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
Justin Kay, Matt Merrifield
Camera-based electronic monitoring (EM) systems are increasingly being deployed onboard commercial fishing vessels to collect essential data for fisheries management and regulation…