FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning

TL;DR

This paper introduces FCL-COD, a weakly supervised camouflage object detection framework that leverages frequency-aware and contrastive learning to improve boundary detection and overall performance, surpassing existing methods.

Contribution

The paper proposes a novel weakly supervised COD method combining frequency-aware adaptation and contrastive learning to address key challenges in camouflage detection.

Findings

Outperforms state-of-the-art weakly supervised methods

Surpasses fully supervised techniques on benchmark datasets

Effectively delineates precise camouflage boundaries

Abstract

Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d. lack of refined boundary awareness, which leads to unsatisfactory results in camouflage scenes. To alleviate these issues, we propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD. To mitigate the problem of non-camouflaged object responses, we propose the Frequency-aware Low-rank Adaptation (FoRA) method, which incorporates frequency-aware camouflage scene knowledge into SAM. To overcome the challenges of local and extreme responses, we introduce a gradient-aware contrastive learning approach that effectively delineates precise foreground-background boundaries. Additionally, to address the lack of refined boundary perception, we present a multi-scale frequency-aware representation learning strategy that facilitates the modeling of more refined boundaries. We validate the effectiveness of our approach through extensive empirical experiments on three widely recognized COD benchmarks. The results confirm that our method surpasses both state-of-the-art weakly supervised and even fully supervised techniques.