# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments
### Ablation Study
Specifically, compared with (a), (c) achieves obvious performance improvements on all datasets, with the performance gains of 8.10%, 9.67%, 11.76%, and 2.03% on average in terms of \(S_{\alpha}\), \(E_{\phi}^{ab}\), \(F_{\beta}^{w}\) and \(M\), respectively. Compared with (b), (c) achieves obvious performance improvements on all datasets, with the performance gains of 2.97%, 3.70%, 6.13%, and 0.87% on average in terms of \(S_{\alpha}\), \(E_{\phi}^{ad}\), \(F_{\beta}^{w}\) and \(M\), respectively. This confirms that the FBM can be beneficial for accurate camouflaged object segmentation. **Effectiveness of CRM. ** The key to CRM is our proposed curiosity operation, which continuously refines segmentation results, and ultimately achieves excellent results. From Table 2, compared with (a), (e) achieves obvious performance improvements on all datasets, with the performance gains of 4.10%, 6.67%, 5.10%, and 0.80% on average in terms of \(S_{\alpha}\), \(E_{\phi}^{ad}\), \(F_{\beta}^{w}\) and \(M\), respectively. Compared with (b), (f) achieves obvious performance improvements on all datasets, with the performance gains of 3.10%, 3.47% and 5.80%, and 1.07% on average in terms of \(S_{\alpha}\), \(E_{\phi}^{ad}\), \(F_{\beta}^{w}\) and \(M\), respectively. From the results of (d-i), it can be seen that the utilization of curiosity operations significantly enhances segmentation performance compared to scenarios where curiosity operations are not employed. Additionally, we visualize the heatmaps of intermediate results in CDNet in Figure 6. The columns from left to right represent the original image, the ground truth, and the outputs of FBM\({}_{4}\), CRM\({}_{3}\), CRM\({}_{2}\), and CRM\({}_{1}\), respectively. It is evident that with successive refinement iterations driven by curiosity, there is a significant reduction in curiosity. Specifically, as the curiosity-refinement operations progress, the model's attention gradually shifts away from easily segmented areas, resulting in a reduction of curiosity toward these regions. Simultaneously, the model intensifies its focus on more challenging areas, characterized by higher curiosity, such as edges, occlusions, and intricate details. Through progressive curiosity-driven refinements, these areas are refined to diminish curiosity and ultimately attain outstanding results. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CAMO-Test(250 Images)**} & \multicolumn{4}{c}{**COD10K-Test(2026 Images)**} & \multicolumn{4}{c}{**NC4K(4121 Images)**} \\ \cline{3-14}  & & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{w}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{w}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{w}\uparrow\) & \(M\downarrow\) \\ \hline (a) & B1 & 0.784 & 0.802 & 0.701 & 0.069 & 0.793 & 0.808 & 0.651 & 0.048 & 0.801 & 0.873 & 0.758 & 0.052 \\ (b) & B2 & 0.832 & 0.887 & 0.784 & 0.057 & 0.847 & 0.874 & 0.716 & 0.035 & 0.853 & 0.901 & 0.779 & 0.042 \\ (c) & B1+B2+FBM & 0.865 & 0.917 & 0.822 & 0.052 & 0.871 & 0.927 & 0.793 & 0.024 & 0.885 & 0.929 & 0.848 & 0.032 \\ (d) & B1+CRM \(w/o\) C & 0.831 & 0.887 & 0.769 & 0.060 & 0.811 & 0.869 & 0.718 & 0.041 & 0.841 & 0.902 & 0.784 & 0.049 \\ (e) & B1+CRM & 0.836 & 0.891 & 0.751 & 0.058 & 0.818 & 0.884 & 0.724 & 0.039 & 0.847 & 0.908 & 0.788 & 0.048 \\ (f) & B2+CRM \(w/o\) C & 0.861 & 0.911 & 0.815 & 0.054 & 0.865 & 0.917 & 0.785 & 0.030 & 0.883 & 0.921 & 0.839 & 0.035 \\ (g) & B2+CRM & 0.865 & 0.916 & 0.819 & 0.052 & 0.869 & 0.921 & 0.789 & 0.023 & 0.891 & 0.929 & 0.845 & 0.031 \\ (h) & B1+B2+FBM+CRM \(w/o\) C & 0.867 & 0.921 & 0.824 & 0.048 & 0.874 & 0.931 & 0.799 & 0.022 & 0.887 & 0.932 & 0.848 & 0.031 \\ (i) & Ours & **0.869** & **0.924** & **0.826** & **0.047** & **0.876** & **0.936** & **0.806** & **0.021** & **0.891** & **0.938** & **0.851** & **0.030** \\ \hline \hline \end{tabular}
\end{table}