# Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data Source: [Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:15.079Z Authors: Juwei Guan, Xiaolin Fang, Donghyun Kim, Haotian Gong, Tongxin Zhu, Zhen Ling, Ming Yang Published: 2025-03-28T06:53:21.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()