## 2 Related Work
This model initially converts features from distinct levels into graphs that encapsulate both shallow and deep semantic information, subsequently redistributing this information. In an effort to minimize losses, each layer acquires additional semantic information from other layers. Building on this, the model is then able to capture the contextual information of the effectively processed information. [PERSON] [13] proposed a new model for SOD. This new model mainly comprises three parts: global context, multi-scale aggregation, and adaptive enhancement. The authors conducted experiments on six public datasets, and the results showed that they were better than previous results across all indicators, obtaining good results with robustness. [PERSON] [14] proposed a salient object detection method to improve the depth calibration and the fusion of RGB-D images, which could effectively enhance performance. The proposal of deep calibration as a learning strategy can enhance the testing effectiveness of salient object detection. A simple and effective fusion cross-model can handle both shallow and deep semantic information, which means it can handle more complex scenes. [PERSON] [15] aimed to deal with the issue of SOD through 3D convolutional neural networks. They established a new order and attempted preprocessing during the encoder part. The effective fusion in the decoder part can better interpret the information of shallow and deep semantics. This method has achieved good results on six publicly available datasets, outperforming other methods. In the direction of Camouflaged Object detection (COD), [PERSON] [16] proposed a Global-Context-Aware Progressive Aggregation Network (GPANet) for SOD, which highlights the most eye-catching objects from images. This network utilizes global contextual information and mixed information to improve the localization and segmentation achievement of salient objects. It can also be used for camouflaged object detection, enhancing the perception ability of camouflaged objects by using depth maps as additional input. [PERSON] [4] proposed a camouflage object segmentation method with interference mining (PFNet) for segmenting camouflage objects from complex scenes. This method imitates the predation process in nature, first locating potential objects from a global perspective through a positioning module (PM) and then gradually refining the segmentation results by focusing on fuzzy areas through a focusing module (FM). He [17] proposed a COD method based on mutual graph learning (MGL) to improve the performance of COD on RGB-D images. This method utilizes two graph-based interaction modules, one for obtaining semantic guidance knowledge from edge information and the other for enhancing object localization by combining real edge priors. This method achieves repeated mutual learning and mutual assistance by clearly inferring the relationship between two tasks. [PERSON] [12] proposed a cross-layer feature pyramid network (CFPN) for SOD. This network allocates features from different levels to all involved levels, allowingeach layer's features to have both semantic and detailed information from other layers, thereby reducing the loss of important information. In order to further explain the challenge of the COD problem, [PERSON] [18] proposed a triple-task learning framework that can simultaneously perform three tasks: localization, segmentation, and sorting of camouflage objects. It indicates the saliency level of camouflage objects relative to the background. The authors believed that binary segmentation settings could not fully understand the concept of camouflage, and saliency models could help design more complex camouflage techniques. They also used an eye tracker to generate localization maps and generate ranking-based training and testing datasets based on instance-level labels. They also contributed the largest COD test set and comprehensively analyzed the performance of the COD model.