10 citations · 21 across the 9 of their papers we have counts for
9 papers
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…
Whombat: An open-source annotation tool for machine learning development in bioacoustics
Santiago Martinez Balvanera, Oisin Mac Aodha, Matthew J. Weldy +3
1. Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high-stake…
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…
VL-Fields: Towards Language-Grounded Neural Implicit Spatial Representations
Nikolaos Tsagkas, Oisin Mac Aodha, Chris Xiaoxuan Lu
We present Visual-Language Fields (VL-Fields), a neural implicit spatial representation that enables open-vocabulary semantic queries. Our model encodes and fuses the geometry of a…
Virtual Occlusions Through Implicit Depth
Jamie Watson, Mohamed Sayed, Zawar Qureshi +4
For augmented reality (AR), it is important that virtual assets appear to `sit among' real world objects. The virtual element should variously occlude and be occluded by real matte…
Visual Knowledge Tracing
Neehar Kondapaneni, Pietro Perona, Oisin Mac Aodha
Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers…