## 2 Related Work
The experimental results indicated that the triple-task learning framework had reached a new state-of-the-art level, making the COD network more interpretable. [PERSON] [19] believed that for camouflaged object detection tasks, it was not only necessary to segment the camouflaged object but also to evaluate the significance of the background in order to design an overall strategy for camouflaged object detection. Moreover, the author of the article also contributed a lot of COD information to analyze the abilities of camouflaged object detection models. [PERSON] [20] proposed a new network structure for detecting hidden objects in natural scenes. This network structure utilizes the Attention-induced Cross-level Fusion Module (ACFM) and Dual-brand Global Context Module (DGCM) to fuse multiple layers of features and generate accurate predictions. They conducted experiments on three commonly used datasets, demonstrating the effectiveness and superiority of C2 FNet. The article also demonstrated the potential of C2 FNet in the application of polyp segmentation. [PERSON] [21] mainly focused on four aspects. A comprehensive introduction and analysis were conducted on 39 existing COD models, from 1998 to 2021, including traditional manual feature-based models and deep learning-based models, and classification and a comparison were made based on different detection mechanisms and strategies. A detailed introduction and evaluation were conducted on four widely used COD datasets, including the construction methods, object categories, number of images, annotation methods, and difficulty. Some improvement suggestions were proposed for the datasets. They also compared the performance of existing COD models on four datasets, including mathematical comparison, image comparison, and effect evaluation, and then analyzed the detection performance of these models on different types of objects. The limitations and challenges of COD were discussed as well, and some future research directions were proposed, such as utilizing multimodal information, designing more complex camouflage techniques, and developing larger and more diverse datasets. [PERSON] [22] proposed a context aggregation-based small object detection method to improve the detection performance of YOLOv3-based detectors for small objects. This method utilizes multi-scale contextual information, including global scene information, local object information, and intermediate region information, to enhance the feature representation of small objects. The method has been tested on multiple public datasets, demonstrating its effectiveness and superiority. [PERSON] [23] proposed a feature-enhanced context-aware network (FECANet) for small sample semantic segmentation to solve the problem of small sample semantic segmentation. The network utilizes multiple layers of contextual information, including global semantic information, local edge information, and intermediate spatial information, to enhance the feature representation of the object. The network also utilizes a self-attention mechanism and a multi-scale fusion module to improve the selectivity and adaptability of features. It has been tested on multiple public datasets, confirming its validity and nobility. The EfficientNets model [24] provides a simple and efficient composite model based on balancing network depth, width, and decomposition rate after scaling existing ConvNets models, and further validates the validity of this method on MobileNets and ResNet. EfficientNet-B7 achieved the most advanced top-1 accuracy of 84.4% and a top-5 accuracy of 97.1% on ImageNet. At the same time, it is 8.4 times smaller than ConvNet and its inference speed is 6.1 times faster. Moreover, it was experimentally confirmed that EfficientNets has excellent transfer ability, achieving extremely high accuracy on CIFAR-100 (91.7%), Flower (98.8%), and another three learning datasets, with a parameter count that is an order of magnitude lower.