Search Results for author:
Found 3 papers, 1 papers with code
RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge
The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes.
Class-incremental Learning via Deep Model Consolidation
The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective.
Boundary-sensitive Network for Portrait Segmentation
Compared to the general semantic segmentation problem, portrait segmentation has higher precision requirement on boundary area.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
