Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
Journal:
arXiv
Published Date:
Mar 28, 2025
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.
