# Curiosity-Driven Camouflaged Object Segmentation
## 3 Methodology
### Fusion Bridge Module  
Fusion Bridge Module (FBM) endeavors to synergistically amalgamate local and global features extracted from CNN and Transformer in each layer. As shown in Figure 2, within the framework of FBMi, the feature \(f_{i}^{C}\), extracted by CNN, is directed into the convolution-BN-Relu operation, followed by the extraction of its local features and global features through the local-global feature block (LGFB). The features extracted by the transformer also perform similar operations. Subsequently, the local and global features of both components are fused via the curiosity fusion block (CFB) to obtain the complete fused features. Notably, the CFB incorporates the concept of \"curiosity\". During the model training process, it intelligently balances and optimizes the weights between the two branches, and the multi-scale features from the two branches complement each other. This dynamic adjustment not only ensures the algorithm's sensitivity in capturing the subtle texture and edge information of the camouflaged object but also maintains the understanding of the overall structure of the objects, thereby promoting the effective fusion of features and segmenting the complete camouflaged objects.  
Specifically, Figure 3a illustrates the detailed structure of the well-designed LGFB includes local feature flow and global feature flow to extract local and global features, respectively. The local feature flow initiates by applying a convolution operation on the input feature and then feeds it into a four-branch pyramid convolution block whose convolution kernel is \(k_{j}\) and the number of groups is \(G_{j}\). Ultimately, local features are obtained through a subsequent layer of convolutional processing. We set \(k_{j},j=\{1,2,3,4\}\) to \(3,5,7,9\) and set \(G_{j},j=\{1,2,3,4\}\) to \(1,2,4,4\), respectively. Each convolution is accompanied by a batch normalization (BN) layer and a ReLU nonlinearity operation. The global feature flow initially employs the adaptive average pooling layer and then adopts upsampling after the operations similar to the local feature flow to extract global features.  
The designed CFB facilitates feature fusion by integrating two distinct types of features, namely, local or global features derived from different encoders, and overall integration of local and global features based on the aforementioned features. The comprehensive depiction of CFB can be found in Figure 3b, and its input takes local features \(f_{i}^{l},i=\{1,2,3,4\}\) and global features \(f_{i}^{g},i=\{1,2,3,4\}\) as an example. We can calculate the curiosity weight (\(cw\)) and the fused features \(f_{i}^{F},i=\{1,2,3,4\}\) in the following ways:  
\[cw=S\Big{(}\Big{|}Conv1\Big{(}f_{i}^{l}\Big{)}-Conv1\Big{(}f_{i}^{g}\Big{)} \Big{|}\Big{)} \tag{1}\]  
\[f_{i}^{\text{F}}=Conv1\Big{(}cw\times f_{i}^{g}+(1-cw)\times f_{i}^{l}\Big{)} \tag{2}\]  
where \(Conv1(\cdot)\) and \(S(\cdot)\) denote the \(1\times 1\) convolution layer and sigmoid layer, respectively.  
Finally, we can obtain the initial map of the objects by applying a \(1\times 1\) convolution on \(f_{4}^{F}\). The \(f_{4}^{F}\) and the initial map would be refined progressively by the following curiosity-refinement modules.