  Few-shot segmentation (FSS) is a dense prediction task that aims to infer the
pixel-wise labels of unseen classes using only a limited number of annotated
images. The key challenge in FSS is to classify the labels of query pixels
using class prototypes learned from the few labeled support exemplars. Prior
approaches to FSS have typically focused on learning class-wise descriptors
independently from support images, thereby ignoring the rich contextual
information and mutual dependencies among support-query features. To address
this limitation, we propose a joint learning method termed Masked Cross-Image
Encoding (MCE), which is designed to capture common visual properties that
describe object details and to learn bidirectional inter-image dependencies
that enhance feature interaction. MCE is more than a visual representation
enrichment module; it also considers cross-image mutual dependencies and
implicit guidance. Experiments on FSS benchmarks PASCAL-$5^i$ and COCO-$20^i$
demonstrate the advanced meta-learning ability of the proposed method.
