UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning

• URL: http://arxiv.org/abs/2506.07087v1
• Date: Sun, 08 Jun 2025 11:22:34 GMT
• Title: UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning
• Authors: Weiqi Yan, Lvhai Chen, Huaijia Kou, Shengchuan Zhang, Yan Zhang, Liujuan Cao,
• Abstract summary: Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels.<n>Existing UCOD methods generate pseudo-labels using fixed strategies and train 1 x1 convolutional layers as a simple decoder.<n>We propose a UCOD method with a teacher-student framework via Dynamic Pseudo-label Learning called UCOD-DPL.
• Score: 27.224713055411105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-labels using fixed strategies and train 1 x1 convolutional layers as a simple decoder, leading to low performance compared to fully-supervised methods. We emphasize two drawbacks in these approaches: 1). The model is prone to fitting incorrect knowledge due to the pseudo-label containing substantial noise. 2). The simple decoder fails to capture and learn the semantic features of camouflaged objects, especially for small-sized objects, due to the low-resolution pseudo-labels and severe confusion between foreground and background pixels. To this end, we propose a UCOD method with a teacher-student framework via Dynamic Pseudo-label Learning called UCOD-DPL, which contains an Adaptive Pseudo-label Module (APM), a Dual-Branch Adversarial (DBA) decoder, and a Look-Twice mechanism. The APM module adaptively combines pseudo-labels generated by fixed strategies and the teacher model to prevent the model from overfitting incorrect knowledge while preserving the ability for self-correction; the DBA decoder takes adversarial learning of different segmentation objectives, guides the model to overcome the foreground-background confusion of camouflaged objects, and the Look-Twice mechanism mimics the human tendency to zoom in on camouflaged objects and performs secondary refinement on small-sized objects. Extensive experiments show that our method demonstrates outstanding performance, even surpassing some existing fully supervised methods. The code is available now.

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