Search Results for author:
Found 2 papers, 2 papers with code
Semantic Cross Attention for Few-shot Learning
Our proposed model uses word-embedding representations as semantic features to help train the embedding network and a semantic cross-attention module to bridge the semantic features into the typical visual modal.
Proxy Network for Few Shot Learning
We conclude that the success of metric-learning based approaches lies in the data embedding, the representative of each class, and the distance metric.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
