GGBDCA:Scene graph generation based on Global Gradient Balanced Distribution and Compound Attention

Scene Graph Generation (SGG) is a key task in computer vision, aimed at automatically extracting objects and their relationships from images. Despite significant advances in SGG, the challenge of long-tail distribution continues to impede accurate prediction of rare relationships. To address this, we propose a novel SGG method based on Global Gradient Balanced Distribution and Compound Attention (GGBDCA). First, we introduce a Transformer-based framework that leverages a compound attention mechanism to extract detailed features. To tackle the long-tail identification problem, we propose a Global Gradient Balanced Distribution (GGBD) algorithm, which converts the long-tail issue into a multi-objective optimization problem. Through Gradient Balance Grouping (GBG), similar categories are clustered to enhance attention on rare classes. Then, the Multi-Gradient Descent Algorithm (MGDA) is employed to solve the multi-objective optimization, while the Adaptive Calibration Function (ACF) dynamically adjusts classification scores to improve the model's generalization. These three core modules - GBG, MGDA, and ACF - work together to balance learning between head and tail classes, focusing more on rare categories and boosting overall performance.