# Camouflaged Object Detection That Does Not Require Additional Priors
## 3 Proposed Method
### Adaptive Feature Fusion Module  
Boundary priors and ranking priors have been employed to assist object detection models [18; 22; 42]. Nevertheless, the fundamental information utilized for detection resides within image features. Given the inherent resemblance between foreground and background features in camouflage images and the potential loss of crucial information during model training, effectively harnessing reliable feature information has proven to be a significant challenge for previous models. In such cases, the integration of additional prior knowledge can indeed lead to substantial improvements in detection performance. Figure 2: **Overview of our framework. ** The proposed CCGNet framework comprises three components: the adaptive feature fusion module (AFFM), the foreground separation module (FSM), and the edge refinement module (ERM). The AFFM assumes a pivotal role in delving into the overall structure of camouflaged objects through the adaptive fusion of multilayer features, thus generating tailored camouflage cues (CC) that align with the model’s learning state. The combined operation of the foreground separation module (FSM) and the edge refinement module (ERM), in tandem with the camouflage cues, results in a significant enhancement of feature representation. However, these supplementary prior features rely on human identification, resulting in increased labor costs, and excessive dependence on this supplementary prior information can potentially hinder the algorithm's adaptability and effectiveness. It is crucial to emphasize that the training data already contain supplementary prior knowledge, which may not be fully retained or optimally utilized due to the model's design. To obtain comprehensive and beneficial information for camouflaged object detection, we introduce the adaptive feature fusion module (AFFM). This module dynamically fuses multilayer features based on the model's learning state regarding the camouflaged object, extracting valuable knowledge for detection. This process ultimately yields comprehensive camouflage cues (CC). The camouflage cues encompass all the knowledge that the model has learned, which is beneficial for camouflaged object detection. The efficient utilization of these camouflage cues can enhance the model's understanding of the overall structure of the camouflaged object. The details of AFFM are described below. As shown in Figure 2, a convolution operation is applied to all input features. Subsequently, all input features are resized. The high-level feature \(\{f_{i}\}_{i=2}^{4}\) is adjusted to size \(\{x_{i}\}_{i=1}^{3}\in\mathbb{R}^{\frac{H}{8}\times\frac{W}{8}\times 256}\), and the low-level feature \(f_{1}\) is adjusted to size \(x_{l}\in\mathbb{R}^{\frac{H}{8}\times\frac{W}{8}\times 128}\). Following that, we leverage the deep layer attention (DLA) [43] mechanism to augment the model's comprehension of the overall structure of camouflaged objects. This involves analyzing the interplay between each feature layer, assigning weights to individual layers according to the significance of the acquired features, and applying weight filtering to extract features pertinent to the camouflaged object. Moreover, within the context of DLA, weight generation entails the computation of correlations between feature layers. These feature layers adeptly encapsulate the current model's learning state with respect to the camouflaged object. Consequently, our feature fusion process closely aligns with the model's learning state concerning the camouflaged object. The computation process of DLA is represented by Equation (1). \[w_{i,j}=Softmax(\phi(x)_{i}\cdot(\phi(x))_{j}^{T}),i,j\in\{1,2,3\} \tag{1}\] \[x_{j}=\beta\sum_{i=1}^{3}w_{i,j}x_{i}+x_{j},x_{i}/x_{j}\in\{x_{1 },x_{2},x_{3}\}\] \[x_{h}=[x_{1};x_{2};x_{3}]\]  
where \(w_{i,j}\) represents the correlation weight between layer i and layer j, \(\phi(\cdot)\) denotes the reshape operations, and \(\beta\) is initially set to 0 and then automatically assigned by the network.