  In visual recognition tasks, few-shot learning requires the ability to learn
object categories with few support examples. Its re-popularity in light of the
deep learning development is mainly in image classification. This work focuses
on few-shot semantic segmentation, which is still a largely unexplored field. A
few recent advances are often restricted to single-class few-shot segmentation.
In this paper, we first present a novel multi-way (class) encoding and decoding
architecture which effectively fuses multi-scale query information and
multi-class support information into one query-support embedding. Multi-class
segmentation is directly decoded upon this embedding. For better feature
fusion, a multi-level attention mechanism is proposed within the architecture,
which includes the attention for support feature modulation and attention for
multi-scale combination. Last, to enhance the embedding space learning, an
additional pixel-wise metric learning module is introduced with triplet loss
formulated on the pixel-level embedding of the input image. Extensive
experiments on standard benchmarks PASCAL-5i and COCO-20i show clear benefits
of our method over the state of the art in few-shot segmentation

Computer Vision and Pattern Recognition