65 citations · 73 across the 6 of their papers we have counts for
10 papers
Efficient Passive Acoustic Monitoring of Killer Whales Using a Two-Stage Detection and Ecotype Classification Cascade
Daniela Ruiz, Manuel Castellote, Zhongqi Miao +5
Passive acoustic monitoring of killer whales is particularly important for conservation of the endangered Southern Resident killer whale population, but requires accurate models th…
Overhead Wildlife Locator (OWL): Benchmarking Weakly Supervised Learning for Aerial Wildlife Surveys
Isai Daniel Chacón, Zhongqi Miao, Bruno Demuro +9
Automated aerial wildlife surveys increasingly rely on deep learning, yet standard object detectors require bounding-box annotations, reported to be up to seven times slower and th…
Project SPARROW and the Future of Conservation Technology
Juan M. Lavista Ferres, Carl Chalmers, Bruno Demuro Segundo +14
Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessi…
A strongly annotated passive acoustic dataset for tropical bird monitoring
Daniela Ruiz, Juan Sebastián Ulloa, Zhongqi Miao +11
Passive acoustic monitoring enables continuous, non-invasive biodiversity assessment across diverse ecosystems. The scale of these datasets has driven the adoption of machine learn…
Tree of Attributes Prompt Learning for Vision-Language Models
Tong Ding, Wanhua Li, Zhongqi Miao +1
Prompt learning has proven effective in adapting vision language models for downstream tasks. However, existing methods usually append learnable prompt tokens solely with the categ…
Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation
Andres Hernandez, Zhongqi Miao, Luisa Vargas +4
The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring. In response, scientists have turned to auto…