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Found 8 papers, 3 papers with code
WildlifeDatasets: An open-source toolkit for animal re-identification
In this paper, we present WildlifeDatasets (https://github. com/WildlifeDatasets/wildlife-datasets) - an open-source toolkit intended primarily for ecologists and computer-vision / machine-learning researchers.
SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
This paper introduces the first public large-scale, long-span dataset with sea turtle photographs captured in the wild -- SeaTurtleID2022 (https://www. kaggle. com/datasets/wildlifedatasets/seaturtleid2022).
SeaTurtleID: A novel long-span dataset highlighting the importance of timestamps in wildlife re-identification
We show that time-unaware (random) splits can lead to performance overestimation of more than 100% compared to the time-aware splits for both feature- and CNN-based re-identification methods.
Adversarial examples by perturbing high-level features in intermediate decoder layers
Due to the projection, all iterations are feasible, and our method always generates adversarial images.
DeepTopPush: Simple and Scalable Method for Accuracy at the Top
Accuracy at the top is a special class of binary classification problems where the performance is evaluated only on a small number of relevant (top) samples.
Nonlinear classifiers for ranking problems based on kernelized SVM
Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples.
General Framework for Binary Classification on Top Samples
Many binary classification problems minimize misclassification above (or below) a threshold.
A Simple Yet Effective Approach to Robust Optimization Over Time
Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time.
