Few-shot semantic segmentation aims to segment the target objects in query
under the condition of a few annotated support images. Most previous works
strive to mine more effective category information from the support to match
with the corresponding objects in query. However, they all ignored the category
information gap between query and support images. If the objects in them show
large intra-class diversity, forcibly migrating the category information from
the support to the query is ineffective. To solve this problem, we are the
first to introduce an intermediate prototype for mining both deterministic
category information from the support and adaptive category knowledge from the
query. Specifically, we design an Intermediate Prototype Mining Transformer
(IPMT) to learn the prototype in an iterative way. In each IPMT layer, we
propagate the object information in both support and query features to the
prototype and then use it to activate the query feature map. By conducting this
process iteratively, both the intermediate prototype and the query feature can
be progressively improved. At last, the final query feature is used to yield
precise segmentation prediction. Extensive experiments on both PASCAL-5i and
COCO-20i datasets clearly verify the effectiveness of our IPMT and show that it
outperforms previous state-of-the-art methods by a large margin. Code is
available at this https URL

本研究提出了一种中介原型矿机器 (IPMT) 来学习和挖掘来自查询和支撑图像中的目标，通过迭代学习实现在查询和支撑图像间分类信息并结合中间原型进一步提高分割预测精确性。