# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 2 Related Work
### Graph Structure Methods for MLIC  
A significant body of work has demonstrated that complex label relationships are well suited for constructing graph structures. [PERSON] et al. [17] created a tree-like structure graph in the label space using the maximum spanning tree algorithm, and they also identified sets of informative label combinations to improve overall multi-label prediction performance. [PERSON] et al. [44] proposed a graphical lasso framework that models label correlations by jointly considering image features and labels. In state-of-the-art approaches [14; 45], GCNs have been introduced for MLIC. The study of [12] utilized GCN to establish a dependency model among labels. The model constructs a directed graph on target labels, where each node is represented by the word embeddings of the label. GCN learns to map this label graph into a set of interdependent target classifiers. Additionally, the model employs a novel reweighting scheme to create an effective label correlation matrix, optimizing the propagation of information between GCN nodes. However, the method does not consider the global nature of image features and label semantics, and it only employs ResNet and standard GCN modules as the backbone network. In this paper, when we established the image spatial feature dependency, we also added FFM for feature fusion and FEM for high-level feature enhancement on top of ResNet. This makes up for the defect of not being able to establish long-range spatial dependencies due to the size limitation of the convolutional kernel. Meanwhile, when modeling inter-label semantic dependencies, we also added our designed GREM after the GCN module to enhance the global inter-label semantics. [PERSON] et al. [19] proposed a transformer-based dual relationship learning framework that constructs complementary relationships by exploring the correlation between a structural relationship graph and a semantic relationship graph. The structural relationship graph aims to capture long-term correlations by leveraging a scale-crossing transformer architecture within the target context. The semantic graph dynamically models the semantic significance of image objects using explicit semantic-aware constraints. However, on the one hand, this method introduces multiple Transformer modules and complex structures, leading to a model that is too large to be used in some scenarios with limited resources and, at the same time, also leading to more difficult model training. On the other hand, the approach uses only standard GCN modules and does not take into account the global nature of inter-label semantics. In this paper, we used FFM and FEM to complete the feature fusion and enhancement, which does not add too much computational burden and can achieve superior results. At the same time, GREM was designed to consider and solve the label semantic global problem.  
Motivated by the aforementioned works, we propose Two-Dimensional Dependency Model (TDDM). Our model leverages a Spatial Feature Dependency Module (SFDM) to extract image information and establish dependencies in the image feature space while utilizing a Label Semantic Dependency Module (LSDM) to establish global label semantic dependencies. The key aspect of TDDM lies in simultaneously considering both the image spatial feature dependencies and global label semantic dependencies. We integrated the outputs of SFDM and LSDM to perform MLIC.