733 citations · 1.4k across the 13 of their papers we have counts for
15 papers
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…
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…
Active Learning-Based Species Range Estimation
Christian Lange, Elijah Cole, Grant Van Horn +1
We propose a new active learning approach for efficiently estimating the geographic range of a species from a limited number of on the ground observations. We model the range of an…
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Elijah Cole, Grant Van Horn, Christian Lange +5
Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has b…
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
Grant Van Horn, Rui Qian, Kimberly Wilber +3
We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fin…
On Label Granularity and Object Localization
Elijah Cole, Kimberly Wilber, Grant Van Horn +6
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled…
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…
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…
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery +3
Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label s…
The iWildCam 2018 Challenge Dataset
Sara Beery, Grant van Horn, Oisin Mac Aodha +1
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it…
Recognition in Terra Incognita
Sara Beery, Grant van Horn, Pietro Perona
It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not ye…
The Devil is in the Tails: Fine-grained Classification in the Wild
Grant Van Horn, Pietro Perona
The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applicat…
The iNaturalist Species Classification and Detection Dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song +6
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imb…
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
Steve Branson, Grant Van Horn, Serge Belongie +1
We propose an architecture for fine-grained visual categorization that approaches expert human performance in the classification of bird species. Our architecture first computes an…