## 2 Related Work
### Semi-Supervised Semantic Segmentation  
Recent studies on semi-supervised semantic segmentation have shown excellent results that are close to the performance of fully supervised semantic segmentation models by applying consistency-based methodologies in various ways. CCT [15] enforces agreement between the results of applying perturbed features of various other kinds, and the results of unperturbed features. CPS [9] uses the network consistency method by forcing consistency on the outputs of two models starting with different initializations. PS-MT [8] uses confidence-weighted cross-entropy loss, which multiplies the cross-entropy loss by the segmentation prediction confidence when calculating the unsupervised loss of unlabeled data. PS-MT additionally enforces consistency for predictions using both network perturbation by two teacher models, input perturbation using CutMix with weak and strong augmentation, and feature perturbation using virtual adversarial training (VAT). U\({}^{2}\)PL [7] uses reliable pseudo-labels by filtering based on the probability distribution entropy of all pixels. U\({}^{2}\)PL additionally uses pseudo-labels for most pixel addresses by pushing unreliable pseudo-labels into a queue composed of negative samples and using them for contrastive loss, which is an unsupervised loss. UniMatch [31] revisits FixMatch, a semi-supervised image classification study, for semi-supervised semantic segmentation research. Interestingly, when the FixMatch study with a simple pipeline is converted to a semi-supervised semantic segmentation scenario, it shows competitive results compared to the SOTA study. However, since FixMatch relies heavily on strong augmentation based on passive design, UniMatch has proposed a Unified Dual-Stream Perturbations approach to mitigate this issue. As a result, the method experimentally reports improved performance by leading to an expanded perturbation space. S4 MC [32] proposes a novel confidence refinement scheme to improve the quality of pseudo-labels for semantic segmentation. Unlike common solutions that do not use pseudo-labels for low-confidence predictions, S4 MC leverages the spatial correlation of labels in segmentation maps by grouping adjacent pixels and considering pseudo-labels collectively. Through this, S4 MC maintains the quality of pseudo-labels while simultaneously increasing the amount of pseudo-labels used during training. Several SOTA studies have shown semi-supervised semantic segmentation results that can be used in practice by using various consistency and pseudo-labels filtering methods.  
However, as discussed in Section 1, technical problems such as overfitting and reliability problems of pseudo-labels have been reviewed so far, but not much has been written about the specific problems of pseudo-labels. We found that a common reason for mispredicted pseudo-labels used in recent SOTA studies is that there is no confusion between different foreground classes, but mostly between each foreground and background class. Therefore, we propose an improved background and foreground binary segmentation model for semi-supervised semantic segmentation. Our approach uses a consistency training approach based on input perturbations and new output perturbations.