MO2.L5.4

SELF-SUPERVISED LEARNING ON CAMERA TRAP FOOTAGE YIELDS A STRONG UNIVERSAL FACE EMBEDDER

Vladimir Iashin, Horace Lee, Dan Schofield, Andrew Zisserman, University of Oxford, United Kingdom
Session:
MO2.L5: Computer Vision for Ecological and Biodiversity Monitoring (CV-EBM) Lecture
Track:
Satellite Workshops
Location:
Dena'ina: Tubughnenq 3
Presentation Time:
Mon, 15 Sep, 10:27 - 10:36 Anchorage Time (UTC -9)
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Session MO2.L5
MO2.L5.1: AUTOMATED CAMERA TRAP ANALYSIS: WHAT MAKES REAL-WORLD DATA HARDER THAN CURATED DATASETS?
Haoyu Chen, Amy Reibman, Purdue University, United States
MO2.L5.2: COMPUTER VISION BASED DETECTION OF SCIARIDS AND PHORIDS FOR PEST MANAGEMENT IN MUSHROOM CULTIVATION
Rohit Venkata Sai Dulam, Michael Crossley, Chandra Kambhamettu, University of Delaware, United States
MO2.L5.3: A Wildlife Camera Trap Dataset for Vulnerable Houbara Bustard Adult, Chick, and Egg Detection
Syed Sadaf Ali, Hind Hatem Bamatraf, Khalifa University, United Arab Emirates; Christelle Lucas, Thibault Dieuleveut, Enrico Sorato, RENECO, United Arab Emirates; Naoufel Werghi, Irfan Hussain, Lakmal Seneviratne, Sajid Javed, Khalifa University, United Arab Emirates
MO2.L5.4: SELF-SUPERVISED LEARNING ON CAMERA TRAP FOOTAGE YIELDS A STRONG UNIVERSAL FACE EMBEDDER
Vladimir Iashin, Horace Lee, Dan Schofield, Andrew Zisserman, University of Oxford, United Kingdom
MO2.L5.5: A MODEL-AGNOSTIC ACTIVE LEARNING APPROACH FOR ANIMAL DETECTION FROM CAMERA TRAPS
Thi Thu Thuy Nguyen, Duc Thanh Nguyen, Deakin University, Australia
MO2.L5.6: HIERARCHICAL TRAINING IMPROVES CONSISTENCY OF POLLEN CLASSIFICATION ON THE FAMILY LEVEL
Salih Numan Büyükbaş, Sabancı University, Turkey; Veysel Süzerer, Bingöl Üniversitesi, Turkey; Mamatha Thota, University of Lincoln, United Kingdom; Petra Bosilj, Maastricht University, Netherlands; Erchan Aptoula, Sabancı University, Turkey
Contacts