# Curiosity-Driven Camouflaged Object Segmentation
## 5 Conclusions  
When humans observe camouflaged objects, they will repeatedly examine and analyze them to accurately identify them when faced with complex areas such as edge details that are highly similar to the background. Inspired by this human brain's cognitive process, we propose a novel camouflaged object-segmentation network, the Curiosity-Driven network (CDNet), to simulate this process.  
We first employ the dual-branch feature encoder to extract local and global features, respectively, by capitalizing the distinctive characteristics of CNN and Transformer backbones. Then, we propose the fusion bridge module to effectively fuse these features, providing effective feature information for subsequent refinement. Furthermore, we design the curiosity-refinement module to iteratively refine initial segmentation results by identifying and addressing curiosity of ambiguous areas, capturing valuable cues for precise and comprehensive segmentation. Extensive comparison experiments and ablation studies show that the proposed CDNet achieves superior performance over other state-of-the-art approaches on three COS benchmark datasets. Compared with the recently proposed state-of-the-art method, our model achieves average performance gains of 1.80% on average for \(S_{\alpha}\). Additionally, the application in downstream tasks also verifies the robustness and effectiveness of our proposed CDNet. Overall, all quantitative and qualitative results provide compelling evidence of the success of our CDNet. In the future, we plan to investigate model lightweight and explore the potential of our method for more real-world applications.  
**Author Contributions:** Conceptualization, M.P., M.S. and Z.W.; methodology, M.P., M.S. and Z.W.; formal analysis, M.P.; investigation, M.P.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, M.P., M.S. and Z.W.; visualization, M.P.; supervision, M.S. and Z.W.; funding acquisition, M.S. and Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (CN) under grant Nos. [62076180] and [62376189].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**MACs**} & \multirow{2}{*}{**Para.**} & \multicolumn{4}{c}{**COD10K-Test(2026 Images)**} \\ \cline{4-7}  & & & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{w}\uparrow\) & \(M\downarrow\) \\ \hline BGNet & 58.45 G & 79.85 M & 0.831 & 0.901 & 0.722 & 0.033 \\ ZoomNet & 95.5 G & **23.38 M** & 0.838 & 0.893 & 0.729 & 0.029 \\ DTINet & 144.68 G & 266.33 M & 0.824 & 0.893 & 0.695 & 0.034 \\ HitNet & 55.95 G & 25.73 M & 0.868 & 0.932 & 0.798 & 0.024 \\ PopNet & 154.88 G & 188.05 M & 0.851 & 0.91 & 0.757 & 0.028 \\ \hline Ours & **38.36 G** & 59.34 M & **0.878** & **0.938** & **0.806** & **0.021** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Computational efficiency comparison of our method with the state-of-the-art methods on the COD10K dataset. \(\uparrow\)/\(\downarrow\) indicating that higher/lower scores are better.