  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.
