Progressive Representation Learning for Weakly-Supervised Camouflaged Object Detection

Weakly-supervised camouflaged object detection aims to achieve performance comparable to fully-supervised methods while relying on minimal, coarse annotations (e.g., points, scribble, and box). However, this task is exceptionally challenging, as it requires models to not only overcome the inherent difficulty of detecting camouflaged objects but also to effectively distinguish foreground from background with limited supervision. To address this challenge, we propose a novel training paradigm called Progressive Representation Learning, which aims to jointly enhance the model's ability to extract discriminative features from both the training strategy and model architecture perspectives. Specifically, in terms of the training strategy, a Progressive Self-Training Alignment (PSTA) method is constructed at the image level to generate multi-level self-constructed data, enabling enhanced detection of challenging camouflaged objects through hierarchical self-learning. From the model architecture, we design a progressive fine-tuning module (Multi-Scale Multi-Resolution LoRA, MSMR) and a Adaptive Frequency-aware Fusion (AFF) module. The former explicitly improves multi-level feature representation during the encoding stage, while the latter focuses on high-frequency information in the fusion stage to boost fine-detail detection. Extensive experimental results demonstrate that our method significantly outperforms existing weakly supervised approaches-achieving an average improvement of 6% on the Fw β metric across three datasets-with fewer parameters. Moreover, it even surpasses some fully supervised state-of-the-art methods on certain metrics, highlighting the effectiveness of our progressive representation learning paradigm. Code and results are publicly available at https://github.com/shuyonggao/PRLNet.