The key challenge for few-shot semantic segmentation (FSS) is how to tailor a
desirable interaction among support and query features and/or their prototypes,
under the episodic training scenario. Most existing FSS methods implement such
support-query interactions by solely leveraging plain operations - e.g., cosine
similarity and feature concatenation - for segmenting the query objects.
However, these interaction approaches usually cannot well capture the intrinsic
object details in the query images that are widely encountered in FSS, e.g., if
the query object to be segmented has holes and slots, inaccurate segmentation
almost always happens. To this end, we propose a dynamic prototype convolution
network (DPCN) to fully capture the aforementioned intrinsic details for
accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is
firstly proposed to generate dynamic kernels from support foreground, then
information interaction is achieved by convolution operations over query
features using these kernels. Moreover, we equip DPCN with a support activation
module (SAM) and a feature filtering module (FFM) to generate pseudo mask and
filter out background information for the query images, respectively. SAM and
FFM together can mine enriched context information from the query features. Our
DPCN is also flexible and efficient under the k-shot FSS setting. Extensive
experiments on PASCAL-5i and COCO-20i show that DPCN yields superior
performances under both 1-shot and 5-shot settings.

针对 few-shot 语义分割的挑战，该论文提出了一种基于动态原型卷积网络 (DPCN) 的解决方案，其中动态卷积模块 (DCM) 从支持前景生成动态内核，而支持激活模块 (SAM) 和特征过滤模块 (FFM) 则一起为查询图像提供丰富的背景信息，实现了更准确的 few-shot 语义分割。DPCN 在 1-shot 和 5-shot 设置下均表现出优越性能。