Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Resolution Data.

Abstract

Camouflaged object detection (COD) relies on multi-granularity structural information and fine-grained details to distinguish objects from highly similar backgrounds. Whereas low-resolution data lacks high-frequency cues such as textures and sharp edges, retaining only coarse structures. These not only weaken discriminative features but also introduce resolution-induced camouflage beyond natural blending. Existing COD methods assume high-resolution data and fail to address this dual-source ambiguity, resulting in significant performance degradation and underscoring the need for approaches that explicitly explore essential spatial priors under low-resolution constraints. Therefore, we propose KRNet, the first framework explicitly designed for COD in low-resolution settings. KRNet presents a Leader-Follower framework where the Leader extracts dual gold-standard distributions: conditional and hybrid, from supporting data to drive the Follower in rectifying knowledge learned from low-resolution data. The framework further benefits from a cross-consistency strategy, and a stronger time-prompt conditional encoder that improve the rectification of these distributions. Extensive experiments on benchmark datasets demonstrate that KRNet outperforms state-of-the-art COD methods and SR-assisted COD approaches, highlighting its effectiveness in tackling the challenges of low-resolution data in COD. Code: https://github.com/whyandbecause/KRNet/tree/main.