通过优化分类器提升隐蔽物体分割性能
Classifier-Centric Adaptive Framework for Open-Vocabulary Camouflaged Object Segmentation
- 以分类器为中心，用轻量文本适配器增强分类能力
- 在OVCamo上cIoU提升至0.493，cSm达0.658
- 适合关注开放词汇分割的视觉算法研究者
开放词汇隐蔽物体分割要求模型能分割训练中未见任意类别的隐蔽物体，对泛化能力要求极高。分析现有方法发现，分类组件显著影响整体分割性能。为此提出一种以分类器为中心的自适应框架，通过轻量级文本适配器和新型分层非对称初始化提升分类能力。该方法在OVCamo基准上相比OVCoser基线显著提升分割性能：cIoU从0.443增至0.493，cSm从0.579升至0.658，cMAE从0.336降至0.239。结果表明，针对性增强分类能力是提升隐蔽物体分割性能的有效路径。
原文摘要 · Abstract (English)
Open-vocabulary camouflaged object segmentation requires models to segment camouflaged objects of arbitrary categories unseen during training, placing extremely high demands on generalization capabilities. Through analysis of existing methods, it is observed that the classification component significantly affects overall segmentation performance. Accordingly, a classifier-centric adaptive framework is proposed to enhance segmentation performance by improving the classification component via a lightweight text adapter with a novel layered asymmetric initialization. Through the classification enhancement, the proposed method achieves substantial improvements in segmentation metrics compared to the OVCoser baseline on the OVCamo benchmark: cIoU increases from 0.443 to 0.493, cSm from 0.579 to 0.658, and cMAE reduces from 0.336 to 0.239. These results demonstrate that targeted classification enhancement provides an effective approach for advancing camouflaged object segmentation performance.
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