65 citations · 161 across the 12 of their papers we have counts for
12 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…
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Zalan Fabian, Zhongqi Miao, Chunyuan Li +10
Due to deteriorating environmental conditions and increasing human activity, conservation efforts directed towards wildlife is crucial. Motion-activated camera traps constitute an…
Open Long-Tailed Recognition in a Dynamic World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (ta…
Iterative Human and Automated Identification of Wildlife Images
Zhongqi Miao, Ziwei Liu, Kaitlyn M. Gaynor +3
Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced autom…
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
Xudong Wang, Long Lian, Zhongqi Miao +2
Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, th…
Open Compound Domain Adaptation
Ziwei Liu, Zhongqi Miao, Xingang Pan +4
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a…
Large-Scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known…