G. Dussert

Zero-shot animal behavior classification with vision-language foundation models

This study uses vision-language foundation models to classify animal behaviors in camera trap images without prior training, achieving over 91% accuracy in three ungulate species.

Being confident in confidence scores : calibration in deep learning models for camera trap image sequences

This study evaluates the calibration of deep learning models for camera trap image sequences, showing that temperature scaling improves confidence score interpretability for ecological applications.

The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images

The paper introduces a classification model for French fauna in camera-trap images, achieving high accuracy and precision, and implements it in a user-friendly software tool.

Deep learning for species identification of modern and fossil rodent molars

This paper develops a deep learning method for identifying rodent species from molar teeth images, achieving performance comparable to geometric morphometrics and demonstrating applicability to fossil and archaeological specimens.

Revisiting giraffe photo-identification using deep learning and network analysis

The paper develops a deep learning pipeline for giraffe re-identification using image similarity networks and CNNs, achieving 90% Top-1 accuracy in identifying known individuals from 4,000 photographs.