  Few-shot segmentation aims at assigning a category label to each image pixel
with few annotated samples. It is a challenging task since the dense prediction
can only be achieved under the guidance of latent features defined by sparse
annotations. Existing meta-learning method tends to fail in generating
category-specifically discriminative descriptor when the visual features
extracted from support images are marginalized in embedding space. To address
this issue, this paper presents an adaptive tuning framework, in which the
distribution of latent features across different episodes is dynamically
adjusted based on a self-segmentation scheme, augmenting category-specific
descriptors for label prediction. Specifically, a novel self-supervised
inner-loop is firstly devised as the base learner to extract the underlying
semantic features from the support image. Then, gradient maps are calculated by
back-propagating self-supervised loss through the obtained features, and
leveraged as guidance for augmenting the corresponding elements in embedding
space. Finally, with the ability to continuously learn from different episodes,
an optimization-based meta-learner is adopted as outer loop of our proposed
framework to gradually refine the segmentation results. Extensive experiments
on benchmark PASCAL-$5^{i}$ and COCO-$20^{i}$ datasets demonstrate the
superiority of our proposed method over state-of-the-art.
