CDC: Enhancing Scene Graph Generation for IoST-Driven Social Behavioral Modeling With Cooperative Dual Classifier

Zhaodi Wang, Biao Leng, Xiaokang Zhou

Abstract: Scene graph generation (SGG) plays an important role in the intelligence of social things (IoST) framework by extracting structured semantic representations from social device data, thereby supporting advanced scene understanding and behavioral-cultural modeling. However, the intrinsic long-tail nature of real-world social device data, coupled with the semantic entanglement between head and tail categories (e.g., “on” versus “standing on”), presents significant challenges for fine-grained SGG. This often results in biased models and suboptimal generalization to rare but semantically informative relations. To address these issues, we propose a novel cooperative dual classifier (CDC) framework for fine-grained SGG in IoST-driven social systems. CDC introduces a cooperative learning mechanism that combines two classifiers. The frozen prototype classifier is designed with maximum interclass margins to alleviate class imbalance. In parallel, a learnable classifier dynamically adjusts decision boundaries to improve discriminative precision. To further enhance the integration between the two classifiers, we introduce a weight knowledge transfer (WKT) module and a collaborative constraint term, facilitating robust adaptation to tail categories. Extensive experiments on the Visual Genome and GQA datasets demonstrate that CDC outperforms state-of-the-art SGG methods, particularly in modeling fine-grained relations under long-tail distributions. These results highlight the capability of CDC to advance semantic understanding of complex behavioral and cultural patterns within computational social systems.