  Few-shot segmentation (FSS) aims to segment novel categories given scarce
annotated support images. The crux of FSS is how to aggregate dense
correlations between support and query images for query segmentation while
being robust to the large variations in appearance and context. To this end,
previous Transformer-based methods explore global consensus either on context
similarity or affinity map between support-query pairs. In this work, we
effectively integrate the context and affinity information via the proposed
novel Context and Affinity Transformer (CATrans) in a hierarchical
architecture. Specifically, the Relation-guided Context Transformer (RCT)
propagates context information from support to query images conditioned on more
informative support features. Based on the observation that a huge feature
distinction between support and query pairs brings barriers for context
knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures
attention-aware affinity as auxiliary information for FSS, in which the
self-affinity is responsible for more reliable cross-affinity. We conduct
experiments to demonstrate the effectiveness of the proposed model,
outperforming the state-of-the-art methods.
