CANet : class-agnostic segmentation networks with iterative refinement and attentive few-shot learning
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this...
| Principais autores: | , , , , |
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| Outros Autores: | |
| Formato: | Conference Paper |
| Idioma: | English |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/144391 |