3 citations · 6 across the 2 of their papers we have counts for
5 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,…
SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance
Suzanne Stathatos, Michael Hobley, Pietro Perona +1
Low signal-to-noise ratio videos -- such as those from underwater sonar, ultrasound, and microscopy -- pose significant challenges for computer vision models, particularly when pai…
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…