We introduce a novel cost aggregation network, dubbed Volumetric Aggregation
with Transformers (VAT), to tackle the few-shot segmentation task by using both
convolutions and transformers to efficiently handle high dimensional
correlation maps between query and support. In specific, we propose our encoder
consisting of volume embedding module to not only transform the correlation
maps into more tractable size but also inject some convolutional inductive bias
and volumetric transformer module for the cost aggregation. Our encoder has a
pyramidal structure to let the coarser level aggregation to guide the finer
level and enforce to learn complementary matching scores. We then feed the
output into our affinity-aware decoder along with the projected feature maps
for guiding the segmentation process. Combining these components, we conduct
experiments to demonstrate the effectiveness of the proposed method, and our
method sets a new state-of-the-art for all the standard benchmarks in few-shot
segmentation task. Furthermore, we find that the proposed method attains
state-of-the-art performance even for the standard benchmarks in semantic
correspondence task although not specifically designed for this task. We also
provide an extensive ablation study to validate our architectural choices. The
trained weights and codes are available at: this https URL

介绍了一种新颖的成本聚合网络 ——Volumetric Aggregation with Transformers (VAT)，通过使用卷积和 Transformer 处理查询和支持之间的高维相关性地图，从而有效地处理 Few-shot 分割任务。