  Multi-Label Image Classification (MLIC) aims to predict a set of labels that
present in an image. The key to deal with such problem is to mine the
associations between image contents and labels, and further obtain the correct
assignments between images and their labels. In this paper, we treat each image
as a bag of instances, and reformulate the task of MLIC as an instance-label
matching selection problem. To model such problem, we propose a novel deep
learning framework named Graph Matching based Multi-Label Image Classification
(GM-MLIC), where Graph Matching (GM) scheme is introduced owing to its
excellent capability of excavating the instance and label relationship.
Specifically, we first construct an instance spatial graph and a label semantic
graph respectively, and then incorporate them into a constructed assignment
graph by connecting each instance to all labels. Subsequently, the graph
network block is adopted to aggregate and update all nodes and edges state on
the assignment graph to form structured representations for each instance and
label. Our network finally derives a prediction score for each instance-label
correspondence and optimizes such correspondence with a weighted cross-entropy
loss. Extensive experiments conducted on various image datasets demonstrate the
superiority of our proposed method.
