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.

我们提出了第一个完全基于图卷积的模型，Group K-nearest neighbor based Graph convolutional Network (GKGNet)，该模型能够在图结构中灵活统一地建模语义标签嵌入和图像补丁之间的连接，通过动态图构建和信息传递实现了在多标签数据集上的最先进性能，同时显著降低计算成本。