4 accepted papers
Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning conven…
Medical image segmentation typically demands extensive dense annotations for model training, which is both time-consuming and skill-intensive. To mitigate this burden, exemplar-based medical image segmentation methods have been introduced to achieve effective training with only one annotated image.…
Instance segmentation seeks to identify and segment each object from images, which often relies on a large number of dense annotations for model training. To alleviate this burden, unsupervised instance segmentation methods have been developed to train class-agnostic instance segmentation models wit…
Few-shot semantic segmentation intends to predict pixel level categories using only a few labeled samples. Existing few-shot methods focus primarily on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured. The actual domain shift problem significa…