  Research into Few-shot Semantic Segmentation (FSS) has attracted great
attention, with the goal to segment target objects in a query image given only
a few annotated support images of the target class. A key to this challenging
task is to fully utilize the information in the support images by exploiting
fine-grained correlations between the query and support images. However, most
existing approaches either compressed the support information into a few
class-wise prototypes, or used partial support information (e.g., only
foreground) at the pixel level, causing non-negligible information loss. In
this paper, we propose Dense pixel-wise Cross-query-and-support Attention
weighted Mask Aggregation (DCAMA), where both foreground and background support
information are fully exploited via multi-level pixel-wise correlations between
paired query and support features. Implemented with the scaled dot-product
attention in the Transformer architecture, DCAMA treats every query pixel as a
token, computes its similarities with all support pixels, and predicts its
segmentation label as an additive aggregation of all the support pixels' labels
-- weighted by the similarities. Based on the unique formulation of DCAMA, we
further propose efficient and effective one-pass inference for n-shot
segmentation, where pixels of all support images are collected for the mask
aggregation at once. Experiments show that our DCAMA significantly advances the
state of the art on standard FSS benchmarks of PASCAL-5i, COCO-20i, and
FSS-1000, e.g., with 3.1%, 9.7%, and 3.6% absolute improvements in 1-shot mIoU
over previous best records. Ablative studies also verify the design DCAMA.
