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Found 3 papers, 1 papers with code
On the Role of Neural Collapse in Meta Learning Models for Few-shot Learning
While these phenomena have been observed in simple classification networks, this study is the first to explore and understand the properties of neural collapse in meta learning frameworks for few-shot learning.
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels.
One-Shot Weakly Supervised Video Object Segmentation
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks.
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