# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 3 CISNet Method
### Cross-Level Iterative Structure  
The highest-level features extracted by the transformer contain the most semantic information, which is beneficial for locating objects, while the lowest-level features contain the most spatial information, which is advantageous for constructing object edge regions. Leveraging this characteristic, we propose a cross-level iterative structure (CIS) to perform the information integration and detail enhancement of cross-level features iteratively. Specifically, CIS integrates the highest-level features and all relevant lower-level features separately at each stage, which is referred to as cross-level integration. This integration approach promotes the mutual sharing of semantic and spatial information between high-level and low-level features. Compared to the integration of adjacent level features, our cross-level integration approach allows for a more comprehensive fusion of semantic and spatial information, utilizing the strengths of both high-level and low-level features to enhance the overall representation of features. To further enrich the multi-level information captured by our approach, we adopt a sequential iteration strategy for cross-level integration. This strategy enables the generation of a series of information-rich features with varying orders and receptive fields. By iteratively refining the integration process, our approach effectively captures a broader range of context information across multiple levels, enabling the acquisition of more comprehensive and semantically expressive feature representation. The features generated at different levels possess unique characteristics. Exploiting the differences between these features can highlight subtle features in objects and their backgrounds. Therefore, we propose the multi-scale strip convolution subtraction (MSCSub), as shown in Figure 2, to emphasize the differences from texture to structure between different levels and capture their complementary information, providing features with rich information for subsequent operations. To cope with the challenges posed by the diverse sizes and shapes of camouflaged objects, as well as the particular \"ruggedness\" of their edges, we utilize strip convolution layers within the MSCSub. Strip convolution uses some convolution kernels of different scales in the horizontal and vertical directions to capture features, which can better capture  
Figure 2: Overall architecture of proposed CISNet. subtle features such as the texture and shape of the target object in various directions. Specifically, it uses longer kernel shapes in one direction and narrower in another to aggregate both global context and local context information. This offers an advantage by capturing remote dependencies between isolated regions with longer kernel shapes in one spatial dimension, while using narrower kernel shapes in other dimensions to effectively capture local context information and prevent interference from extraneous regions. Thereby, it enhances the ability to capture details along the object boundaries. Furthermore, considering that the receptive field within a single-scale convolution kernel is limited, we employ strip convolutions at four sizes of \(1\times 3\), \(3\times 1\), \(1\times 5\), and \(5\times 1\) in addition to \(1\times 1\). After obtaining multi-scale features, we directly subtract features that have the same scale to capture the complementarity information between high-level and low-level features and highlight detail and structure differences according to the pixel-pixel and region-region patterns. In this way, MSCSub introduces strip convolution kernels of different sizes to adaptively capture contextual information at different scales and perform multi-scale features interacting with each other, thereby generating more effective and discriminating feature information. The entire multi-scale strip convolution subtraction process can be formulated as follows:  
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