Query-guided Prototype Evolution Network for Few-Shot Segmentation
Runmin Cong Hang Xiong Jinpeng Chen Wei Zhang Qingming Huang Yao Zhao
Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support
features for prototype generation, neglecting the specific requirements of the
query. To address this, we present the Query-guided Prototype Evolution Network
(QPENet), a new method that integrates query features into the generation
process of foreground and background prototypes, thereby yielding customized
prototypes attuned to specific queries. The evolution of the foreground
prototype is accomplished through a \textit{support-query-support} iterative
process involving two new modules: Pseudo-prototype Generation (PPG) and Dual
Prototype Evolution (DPE). The PPG module employs support features to create an
initial prototype for the preliminary segmentation of the query image,
resulting in a pseudo-prototype reflecting the unique needs of the current
query. Subsequently, the DPE module performs reverse segmentation on support
images using this pseudo-prototype, leading to the generation of evolved
prototypes, which can be considered as custom solutions. As for the background
prototype, the evolution begins with a global background prototype that
represents the generalized features of all training images. We also design a
Global Background Cleansing (GBC) module to eliminate potential adverse
components mirroring the characteristics of the current foreground class.
Experimental results on the PASCAL-$5^i$ and COCO-$20^i$ datasets attest to the
substantial enhancements achieved by QPENet over prevailing state-of-the-art
techniques, underscoring the validity of our ideas.