C$^2$Path: Class-Conditional Pathway Decoupling for Vision-Language Incremental Object Detection

2026-08-22

AI Summary

本文提出C$^2$Path，通过类别条件路径解耦解决增量目标检测中的类别知识耦合问题，利用类别特异性计算路径实现隔离参数更新和分离注入。

Abstract

Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. However, existing methods suffer from two forms of \textbf{class knowledge coupling}: class boundary erosion induced by shared parameter updates and class representation entanglement arising from mixed feature encoding. We argue that effective incremental learning requires class-specific computational pathways that enable isolated parameter updates and separated class-wise injection. To this end, we propose \textbf{C$^2$Path}, a class-conditional pathway decoupling framework for vision-language incremental object detection that leverages token-level class cues to establish dedicated and updatable computational pathways for different categories. Specifically, C$^2$Path introduces a category expert library and a class-conditional decoupling module. The expert library consists of learnable low-rank computational nodes that capture category-specific knowledge, while the decoupling module generates class-aware routing signals to dynamically compose \textit{ClassLoRA} adapters from these experts, thereby forming class-specific computational pathways for isolated updates and separated injection across categories. Extensive experiments on COCO 2017 under multiple incremental learning settings demonstrate that C$^2$Path consistently outperforms state-of-the-art methods, providing an effective and scalable solution for continual category expansion in vision-language detectors.

Problem

• Incremental Object Detection
• class knowledge coupling
• class boundary erosion
• class representation entanglement

Innovation

• Class-Conditional Pathway Decoupling
• Incremental Object Detection
• Category Expert Library
• Class-Conditioned Decoupling Module
• ClassLoRA Adapters