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Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
[article]
2020
arXiv
pre-print
Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-level, scribbles and bounding box supervision. This paper takes another approach, i.e., only requiring image-level label for few-shot object segmentation. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding. Our
arXiv:2001.09540v3
fatcat:tw7bceylr5bslhyn76msotbl3e