  Few-shot segmentation has been attracting a lot of attention due to its
effectiveness to segment unseen object classes with a few annotated samples.
Most existing approaches use masked Global Average Pooling (GAP) to encode an
annotated support image to a feature vector to facilitate query image
segmentation. However, this pipeline unavoidably loses some discriminative
information due to the average operation. In this paper, we propose a simple
but effective self-guided learning approach, where the lost critical
information is mined. Specifically, through making an initial prediction for
the annotated support image, the covered and uncovered foreground regions are
encoded to the primary and auxiliary support vectors using masked GAP,
respectively. By aggregating both primary and auxiliary support vectors, better
segmentation performances are obtained on query images. Enlightened by our
self-guided module for 1-shot segmentation, we propose a cross-guided module
for multiple shot segmentation, where the final mask is fused using predictions
from multiple annotated samples with high-quality support vectors contributing
more and vice versa. This module improves the final prediction in the inference
stage without re-training. Extensive experiments show that our approach
achieves new state-of-the-art performances on both PASCAL-5i and COCO-20i
datasets.
