GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for
  Multi-Label Image Recognition
Ruijie Yao Sheng Jin Lumin Xu Wang Zeng Wentao Liu Chen Qian Ping Luo Ji Wu
Multi-Label Image Recognition (MLIR) is a challenging task that aims to
predict multiple object labels in a single image while modeling the complex
relationships between labels and image regions. Although convolutional neural
networks and vision transformers have succeeded in processing images as regular
grids of pixels or patches, these representations are sub-optimal for capturing
irregular and discontinuous regions of interest. In this work, we present the
first fully graph convolutional model, Group K-nearest neighbor based Graph
convolutional Network (GKGNet), which models the connections between semantic
label embeddings and image patches in a flexible and unified graph structure.
To address the scale variance of different objects and to capture information
from multiple perspectives, we propose the Group KGCN module for dynamic graph
construction and message passing. Our experiments demonstrate that GKGNet
achieves state-of-the-art performance with significantly lower computational
costs on the challenging multi-label datasets, \ie MS-COCO and VOC2007
datasets. We will release the code and models to facilitate future research in
this area.