# Camouflaged Object Detection That Does Not Require Additional Priors
## 2 Related Work
### Camouflaged Object Detection  
In recent years, COD methods can be categorized into two groups: those utilizing prior information and those not relying on prior information.  
**With additional prior information.** [PERSON] et al. [18; 22] introduced a joint framework for locating, segmenting, and ranking camouflaged objects. They incorporated additional ranking information during training, improving camouflage understanding. [PERSON] et al. [21] proposed mutual graph learning, which effectively separates images into task-specific feature maps for precise localization and boundary refinement. [PERSON] et al. [20] explored the use of object-related edge semantics as an additional guide for model learning, encouraging the generation of features that emphasize the object's edges. [PERSON] et al. [25] suggested using edge likelihood maps for guiding the fusion of camouflaged object features, aiming to enhance detection performance by improving boundary details. [PERSON] et al. [26] employed a pseudo-edge generator to predict edge labels, contributing to accurate edge predictions. [PERSON] et al. [27] proposed the utilization of Canny edge [28] and Conedge techniques to assist in model training. [PERSON] et al. [29] proposed co-training camouflage and saliency objects [30; 31] to enhance model detection. [PERSON] et al. [5] integrated the advantages of Bayesian learning and transformer-based [32; 33] inference. They introduced uncertainty-guided random masking as prior knowledge to facilitate model training. [PERSON] et al. [34] utilized edge information to assess the degree of concealment of the object. [PERSON] et al. [35] suggested the selection of certain structural features, such as illumination, texture direction, and edges, and employed weighted structural texture similarity to assess the impact of camouflage texture. However, prior information is often expensive and impractical.  
**Without additional prior information.** [PERSON] et al. [36] proposed a localization module, a focus module, and a novel distraction mining strategy to enhance model performance. [PERSON] et al. [7; 19] introduced a search and recognition network inspired by the predatory behavior of hunters in nature, which implements object localization and recognition steps. [PERSON] et al. [37] proposed an attention-inducing fusion module that integrates multilevel features and incorporates contextual information for more effective prediction. [PERSON] et al. [10] proposed a model that incorporates two processes of predation, specifically sensory and cognitive mechanisms. To achieve this, specialized modules were designed to selectively and attentively aggregate initial features using an attention-based approach. [PERSON] et al. [38] proposed a method where the model attends to fixation and edge regions and utilizes an attention-based sampler to progressively zoom in on the object region instead of increasing the image size. This approach allows for the iterative refinement of features. [PERSON] et al. [39] introduced the concept of constructing multiple texture-aware refinement modules within a deep convolutional neural network. These modules aim to learn texture-aware features that can accentuate subtle texture differences between the camouflaged object and the background. [PERSON] et al. [40] integrated large receptive fields and effective feature fusion into a unified framework to enhance the model's ability to detect camouflaged objects. In practice, algorithms that do not depend on prior information typically utilize various techniques to aggregate features with different receptive field sizes to obtain better detection results. Many of these algorithms exhibit inefficiency and frequently encounter limitations in the efficient extraction of image information. In contrast, our proposed CCGNet effectively guides the model training process by autonomously extracting valuable features from the image itself, generating camouflage cues without the need for external prior knowledge.