Semi-supervised Iterative Learning Network for Camouflaged Object Detection

Current camouflaged object detection (COD) methods rely heavily on large-scale datasets with pixel-level annotations. We propose a semi-supervised iterative learning network (SILNet) to address the reliance on large-scale pixel-level annotations in COD. SILNet employs a co-training strategy with convolutional networks and Transformers as encoders, followed by a binary gated decoder (BGD) for feature fusion. To optimize the use of labeled data, we introduce an optimal representative election mechanism (OREM) to identify key sequences of unlabeled images, guiding iterative learning and pseudo-label generation. To reduce noise in pseudo-labels, we incorporate a long-range representation module (LRM) leveraging Mamba’s background modeling. Experiments show that SILNet trained with only 10% of the labeled data outperforms state-of-theart unsupervised and weakly supervised methods, achieving performance competitive with fully supervised models.