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Found 6 papers, 3 papers with code
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
In particular, we instruction tune vision-language models to generate detailed visual descriptions of camera trap images using similar terminology to experts.
Open Long-Tailed Recognition in a Dynamic World
A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes).
Iterative Human and Automated Identification of Wildlife Images
Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation.
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
We take a dynamic view of the training data and provide a principled model bias and variance analysis as the training data fluctuates: Existing long-tail classifiers invariably increase the model variance and the head-tail model bias gap remains large, due to more and larger confusion with hard negatives for the tail.
Ranked #22 on
Long-tail Learning
on iNaturalist 2018
Open Compound Domain Adaptation
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 target domain (e. g., rainy weather).
Large-Scale Long-Tailed Recognition in an Open World
We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.
