Search Results for author:
Found 5 papers, 3 papers with code
Active Learning Guided Federated Online Adaptation: Applications in Medical Image Segmentation
Motivated by these, we propose a method for medical image segmentation that adapts to each incoming data batch (online adaptation), incorporates physician feedback through active learning, and assimilates knowledge across facilities in a federated setup.
Unbiased Scene Graph Generation in Videos
The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG.
Reconstruction guided Meta-learning for Few Shot Open Set Recognition
However, it is a critical task in many applications like environmental monitoring, where the number of labeled examples for each class is limited.
Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning nature of SVMs.
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification.
