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

2025-03-28

AI Summary

Low-quality images suffer from severe detail degradation, significantly impeding camouflaged object detection (COD), whereas existing methods are predominantly designed for high-fidelity inputs and thus exhibit substantial performance drops under degraded conditions. To address this, we propose KRNet—the first dedicated framework for low-quality COD—featuring a novel Leader-Follower collaborative architecture that guides knowledge correction. We introduce a dual gold-standard distribution paradigm—comprising a conditional distribution and a mixture distribution—to drive robust knowledge correction. Further, we design a cross-consistency constraint and a temporally dependent conditional encoder to enhance distributional diversity and correction robustness, and integrate unsupervised knowledge distillation for efficient adaptation. Extensive experiments on multiple benchmarks demonstrate that KRNet consistently outperforms state-of-the-art COD methods and super-resolution–based auxiliary approaches, validating its effectiveness and generalizability for detecting camouflaged objects in low-quality imagery.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

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.

Problem

• Addresses camouflaged object detection in low-quality data
• Rectifies knowledge gaps between high and low-quality data
• Improves detection accuracy via dual gold-standard distributions

Innovation

• Leader-Follower framework for knowledge rectification
• Cross-consistency strategy improves distribution rectification
• Time-dependent conditional encoder enhances diversity