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 unlabele...
