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Found 3 papers, 1 papers with code
Activating the Discriminability of Novel Classes for Few-shot Segmentation
To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
In this work we design a novel knowledge distillation framework to guide the learning of the object detector and thereby restrain the overfitting in both the pre-training stage on base classes and fine-tuning stage on novel classes.
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy.
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