针对低质图像中的伪装目标检测难题，提出首个专门框架提升模型性能。
Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
- 设计领导-跟随框架，从高质量数据中提取双标准分布指导低质数据学习。
- 在多个基准数据集上超越现有最先进方法，尤其在低质图像上表现显著提升。
- 适合处理图像质量差的伪装目标检测场景，如红外或模糊图像应用。
低质量数据常因细节不足而引入额外伪装因素，加剧伪装目标检测（COD）难度。现有方法主要针对高质量数据，忽视低质量数据带来的挑战，导致性能严重下降。为此，我们提出KRNet，首个专为低质量数据设计的COD框架。KRNet采用领导-跟随架构，由领导者从高质量数据中提取条件与混合双黄金标准分布，引导跟随者修正从低质量数据中学到的知识。框架还引入跨一致性策略优化分布校正，并采用时间依赖的条件编码器增强分布多样性。大量实验表明，KRNet在多个基准数据集上优于当前最优的COD方法及超分辨率辅助方法，验证了其在应对低质量数据挑战上的有效性。
原文摘要 · Abstract (English)
Low-quality data often suffer from insufficient image details, introducing an extra implicit aspect of camouflage that complicates camouflaged object detection (COD). Existing COD methods focus primarily on high-quality data, overlooking the challenges posed by low-quality data, which leads to significant performance degradation. Therefore, we propose KRNet, the first framework explicitly designed for COD on low-quality data. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from high-quality data to drive the Follower in rectifying knowledge learned from low-quality data. The framework further benefits from a cross-consistency strategy that improves the rectification of these distributions and a time-dependent conditional encoder that enriches the distribution diversity. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and super-resolution-assisted COD approaches, proving its effectiveness in tackling the challenges of low-quality data in COD.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。