1 citations · 2 across the 16 of their papers we have counts for
20 papers
Mind the Tail: Data Selection Through Sparse Concepts
Ziheng Zhang, Neha Hulkund, Colin Lee +7
Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation strategies may produce vari…
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…
BioDCASE: Active Learning for Bioacoustics
Ben McEwen, Rupa Kurinchi-Vendhan, Shiqi Zhang +3
Ecological monitoring increasingly relies on machine learning models, whose performance depends on the quality and quantity of labelled data. However, obtaining these labels is cos…
Autonomous Scientific Discovery via Iterative Meta-Reflection
Bingchen Zhao, Sara Beery, Oisin Mac Aodha
Autonomous scientific discovery systems offer the potential to accelerate research by automating the process of hypothesis generation and validation. However, current systems opera…
Finding Needles in the Haystack: Transductive Active Labeling in Ecology
Rupa Kurinchi-Vendhan, Sara Beery
Active learning is now standard practice in labeling ecological data, enabling ecologists to quickly process large volumes of field data to understand and monitor natural environme…
Centering Ecological Goals in Automated Identification of Individual Animals
Lukas Picek, Timm Haucke, Lukáš Adam +16
Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent…
ID-Sim: An Identity-Focused Similarity Metric
Julia Chae, Nicholas Kolkin, Jui-Hsien Wang +3
Humans have remarkable selective sensitivity to identities -- easily distinguishing between highly similar identities, even across significantly different contexts such as diverse…
TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life
Mridul Khurana, Amin Karimi Monsefi, Justin Lee +9
Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the rema…
Deep in the Jungle: Towards Automating Chimpanzee Population Estimation
Tom Raynes, Otto Brookes, Timm Haucke +6
The estimation of abundance and density in unmarked populations of great apes relies on statistical frameworks that require animal-to-camera distance measurements. In practice, acq…
Finer-Personalization Rank: Fine-Grained Retrieval Examines Identity Preservation for Personalized Generation
Connor Kilrain, David Carlyn, Julia Chae +3
The rise of personalized generative models raises a central question: how should we evaluate identity preservation? Given a reference image (e.g., one's pet), we expect the generat…
INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases
Edward Vendrow, Julia Chae, Rupa Kurinchi-Vendhan +10
Many ecological questions center on complex phenomena, such as species interactions, behaviors, phenology, and responses to disturbance, that are inherently difficult to observe an…
Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
Olawale Salaudeen, Haoran Zhang, Kumail Alhamoud +2
Benchmarks for out-of-distribution (OOD) generalization frequently show a strong positive correlation between in-distribution (ID) and OOD accuracy across models, termed "accuracy-…
ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition
Abdulwahab Felemban, Yahia Battach, Faizan Farooq Khan +10
Coral reefs are rapidly declining under anthropogenic pressures (e.g., climate change), creating an urgent need for scalable and automated monitoring. Progress in data-driven coral…
DataS^3: Dataset Subset Selection for Specialization
Neha Hulkund, Alaa Maalouf, Levi Cai +15
In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on s…
Pairwise Matching of Intermediate Representations for Fine-grained Explainability
Lauren Shrack, Timm Haucke, Antoine Salaün +2
The differences between images belonging to fine-grained categories are often subtle and highly localized, and existing explainability techniques for deep learning models are often…
Do Large Language Model Benchmarks Test Reliability?
Joshua Vendrow, Edward Vendrow, Sara Beery +1
When deploying large language models (LLMs), it is important to ensure that these models are not only capable, but also reliable. Many benchmarks have been created to track LLMs' g…
Personalized Representation from Personalized Generation
Shobhita Sundaram, Julia Chae, Yonglong Tian +2
Modern vision models excel at general purpose downstream tasks. It is unclear, however, how they may be used for personalized vision tasks, which are both fine-grained and data-sca…
Adapting the re-ID challenge for static sensors
Avirath Sundaresan, Jason R. Parham, Jonathan Crall +9
In both 2016 and 2018, a census of the highly-endangered Grevy's zebra population was enabled by the Great Grevy's Rally (GGR), a citizen science event that produces population est…
Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrieval
Vaibhav Balloli, Sara Beery, Elizabeth Bondi-Kelly
Image retrieval plays a pivotal role in applications from wildlife conservation to healthcare, for finding individual animals or relevant images to aid diagnosis. Although deep lea…