Geometry Meets Semantics: Fractional Gradient Stabilization for Semantic-Driven Bounding Box Optimization in Visual Detection Tasks

2026-07-26

AI Summary

This work addresses the limitations of existing oriented bounding box regression methods, which rely solely on geometric information while neglecting semantic cues and suffer from unstable gradients during orientation optimization, leading to convergence oscillations. To overcome these issues, the authors propose Fractional-order Semantic IoU loss (FrSIoU), which, for the first time, integrates semantic similarity into IoU-based optimization. By leveraging fractional-order calculus to accumulate historical IoU states, FrSIoU establishes an adaptive gradient gating mechanism that unifies semantic and geometric modeling. Extensive experiments demonstrate consistent performance gains across diverse bounding box representations and visual detection tasks, effectively mitigating gradient oscillations and validating the method’s efficacy and generalization capability.

Abstract

Bounding boxes are fundamental for object localization in visual detection tasks. Among them, oriented bounding boxes are widely used in visual detection tasks, which provide a more precise directional representation. Generally, IoU-based losses are widely adopted to optimize box regression. However, we observed that IoU-driven box optimization suffers from two key issues: (1) it relies solely on geometric properties while ignoring semantic cues; (2) orientation optimization suffers from unstable gradients, causing oscillations in orientation convergence. In this paper, we propose a Fractional Semantic IoU loss to achieve unified semantic-geometric learning with gradient stabilization. First, we design a semantic similarity metric to guide IoU optimization, building a Semantic IoU loss (SIoU loss) with an adaptive gradient gating mechanism. Then, we revisit the gradient instability issue in oriented box optimization and extend the SIoU loss to a fractional-order formulation to build the \textbf{Fr}actional \textbf{S}emantic \textbf{IoU} \textbf{loss} (FrSIoU loss). The FrSIoU loss accumulates historical IoU states to regularize abnormal gradients during bounding box optimization process. Extensive experiments demonstrate that our approach achieves stable performance gains across different bounding box formulations and diverse visual detection tasks. The code will be available on GitHub.

Problem

• oriented bounding box
• IoU-based loss
• gradient instability
• semantic cues
• box regression

Innovation

• Fractional-order optimization
• Semantic-geometric fusion
• Gradient stabilization
• Oriented bounding box
• IoU-based loss