733 citations · 1.3k across the 29 of their papers we have counts for
35 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,…
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…
Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification
Hugo Markoff, Christoph Praschl, Anton Hjalte Jørgensen +5
Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seas…
When One Modality Is Not Enough: Multimodal Sex and Life-Stage Classification of Red Deer from Aerial RGB-Thermal Video
Hugo Markoff, Christoph Praschl, Ivan Ludoški +3
Aerial drone surveys increasingly support wildlife population estimation, yet a useful census is more than a count: population dynamics are defined by species composition, sex rati…
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…
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
Yuyan Chen, Nico Lang, B. Christian Schmidt +5
Global biodiversity is declining at an unprecedented rate, yet little information is known about most species and how their populations are changing. Indeed, some 90% of Earth's sp…
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…
INQUIRE: A Natural World Text-to-Image Retrieval Benchmark
Edward Vendrow, Omiros Pantazis, Alexander Shepard +5
We introduce INQUIRE, a text-to-image retrieval benchmark designed to challenge multimodal vision-language models on expert-level queries. INQUIRE includes iNaturalist 2024 (iNat24…
Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors
Jae Joong Lee, Bosheng Li, Sara Beery +4
We introduce Tree D-fusion, featuring the first collection of 600,000 environmentally aware, 3D simulation-ready tree models generated through Diffusion priors. Each reconstructed…
Visually Consistent Hierarchical Image Classification
Seulki Park, Youren Zhang, Stella X. Yu +2
Hierarchical classification predicts labels across multiple levels of a taxonomy, e.g., from coarse-level 'Bird' to mid-level 'Hummingbird' to fine-level 'Green hermit', allowing f…
Application-Driven Innovation in Machine Learning
David Rolnick, Alan Aspuru-Guzik, Sara Beery +8
In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning prolifer…
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…
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…
MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding
Jun Chen, Ming Hu, Darren J. Coker +5
Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function. Automatic recognition of a…
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh +4
Recent vision architectures and self-supervised training methods enable vision models that are extremely accurate and general, but come with massive parameter and computational cos…
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…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Devis Tuia, Benjamin Kellenberger, Sara Beery +15
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging device…
Domain Adaptation for Rare Classes Augmented with Synthetic Samples
Tuhin Das, Robert-Jan Bruintjes, Attila Lengyel +2
To alleviate lower classification performance on rare classes in imbalanced datasets, a possible solution is to augment the underrepresented classes with synthetic samples. Domain…
Species Distribution Modeling for Machine Learning Practitioners: A Review
Sara Beery, Elijah Cole, Joseph Parker +2
Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that c…
ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification
Peter Kulits, Jake Wall, Anka Bedetti +2
African elephants are vital to their ecosystems, but their populations are threatened by a rise in human-elephant conflict and poaching. Monitoring population dynamics is essential…
Image-to-Image Translation of Synthetic Samples for Rare Classes
Edoardo Lanzini, Sara Beery
The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have onl…
Can poachers find animals from public camera trap images?
Sara Beery, Elizabeth Bondi
To protect the location of camera trap data containing sensitive, high-target species, many ecologists randomly obfuscate the latitude and longitude of the camera when publishing t…