  Few-shot segmentation is a challenging task that aims to segment objects of
new classes given scarce support images. In the inductive setting, existing
prototype-based methods focus on extracting prototypes from the support images;
however, they fail to utilize semantic information of the query images. In this
paper, we propose Bi-level Optimization (BiOpt), which succeeds to compute
class prototypes from the query images under inductive setting. The learning
procedure of BiOpt is decomposed into two nested loops: inner and outer loop.
On each task, the inner loop aims to learn optimized prototypes from the query
images. An init step is conducted to fully exploit knowledge from both support
and query features, so as to give reasonable initialized prototypes into the
inner loop. The outer loop aims to learn a discriminative embedding space
across different tasks. Extensive experiments on two benchmarks verify the
superiority of our proposed BiOpt algorithm. In particular, we consistently
achieve the state-of-the-art performance on 5-shot PASCAL-$5^i$ and 1-shot
COCO-$20^i$.
