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Found 4 papers, 1 papers with code
Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem.
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios
We show that low-level feature transforms play a pivotal role in this performance difference, postulate a new property of augmentations related to their data efficiency, and propose new ways to measure the diversity and realism of augmentations.
CoMaL Tracking: Tracking Points at the Object Boundaries
Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.
CoMIC: Good features for detection and matching at object boundaries
Feature or interest points typically use information aggregation in 2D patches which does not remain stable at object boundaries when there is object motion against a significantly varying background.
