Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
This addresses the problem of performance drop in camouflaged object detection for low-quality data, representing a domain-specific incremental advance.
The paper tackles camouflaged object detection on low-quality data, which causes performance degradation, by proposing KRNet, a framework that uses high-quality data to rectify knowledge from low-quality data, achieving state-of-the-art results on benchmark datasets.
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.