# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**CAMO-Test(250 Images)**} & \multicolumn{4}{c}{**COD10K-Test(2026 Images)**} & \multicolumn{4}{c}{**NC4K(4121 Images)**} \\ \cline{2-13}  & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{av}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{av}\uparrow\) & \(M\downarrow\) & \(S_{\alpha}\uparrow\) & \(F_{\phi}^{ad}\uparrow\) & \(F_{\beta}^{av}\uparrow\) & \(M\downarrow\) \\ \hline \multicolumn{13}{c}{**CNN-Based Method**} \\ \hline SINet & 0.751 & 0.771 & 0.606 & 0.100 & 0.771 & 0.806 & 0.551 & 0.051 & 0.808 & 0.871 & 0.769 & 0.058 \\ C2 FNet & 0.796 & 0.865 & 0.719 & 0.080 & 0.813 & 0.886 & 0.686 & 0.036 & 0.838 & 0.901 & 0.762 & 0.049 \\ TINet & 0.781 & 0.847 & 0.678 & 0.087 & 0.793 & 0.848 & 0.635 & 0.042 & 0.829 & 0.882 & 0.734 & 0.055 \\ JSCOD & 0.800 & 0.872 & 0.728 & 0.073 & 0.809 & 0.882 & 0.684 & 0.035 & 0.842 & 0.906 & 0.771 & 0.047 \\ PFNet & 0.782 & 0.852 & 0.695 & 0.085 & 0.800 & 0.868 & 0.660 & 0.040 & 0.829 & 0.887 & 0.745 & 0.053 \\ LSR & 0.793 & 0.826 & 0.725 & 0.085 & 0.793 & 0.868 & 0.658 & 0.041 & 0.839 & 0.883 & 0.779 & 0.053 \\ R-MGL & 0.775 & 0.848 & 0.673 & 0.088 & 0.814 & 0.865 & 0.666 & 0.035 & 0.833 & 0.890 & 0.740 & 0.052 \\ S-MGL & 0.772 & 0.850 & 0.664 & 0.089 & 0.811 & 0.851 & 0.655 & 0.037 & 0.829 & 0.885 & 0.731 & 0.055 \\ UGTR & 0.785 & 0.859 & 0.686 & 0.086 & 0.818 & 0.850 & 0.667 & 0.035 & 0.839 & 0.886 & 0.746 & 0.052 \\ NCHIT & 0.784 & 0.841 & 0.652 & 0.088 & 0.792 & 0.794 & 0.591 & 0.046 & 0.830 & 0.872 & 0.710 & 0.058 \\ ERRNet & 0.779 & 0.855 & 0.679 & 0.085 & 0.786 & 0.845 & 0.630 & 0.043 & 0.827 & 0.892 & 0.737 & 0.054 \\ TPRNet & 0.814 & 0.870 & 0.781 & 0.076 & 0.829 & 0.892 & 0.725 & 0.034 & 0.854 & 0.903 & 0.790 & 0.047 \\ FAPNet & 0.815 & 0.877 & 0.734 & 0.076 & 0.822 & 0.875 & 0.694 & 0.036 & 0.851 & 0.903 & 0.775 & 0.047 \\ BSANet & 0.794 & 0.866 & 0.717 & 0.079 & 0.818 & 0.894 & 0.699 & 0.034 & 0.841 & 0.906 & 0.771 & 0.048 \\ CCENet & 0.807 & 0.767 & 0.866 & 0.075 & 0.832 & 0.745 & 0.890 & 0.032 & 0.857 & 0.817 & 0.899 & 0.044 \\ PreyNet & 0.790 & 0.856 & 0.708 & 0.077 & 0.813 & 0.894 & 0.697 & 0.034 & 0.834 & 0.899 & 0.763 & 0.050 \\ SINetV2 & 0.820 & 0.882 & 0.743 & 0.070 & 0.815 & 0.887 & 0.680 & 0.037 & 0.847 & 0.903 & 0.769 & 0.048 \\ BGNet & 0.812 & 0.870 & 0.749 & 0.073 & 0.831 & 0.901 & 0.722 & 0.033 & 0.851 & 0.907 & 0.788 & 0.044 \\ SeMaR & 0.815 & 0.881 & 0.753 & 0.071 & 0.833 & 0.893 & 0.724 & 0.034 & 0.841 & 0.905 & 0.781 & 0.046 \\ FDNet & 0.841 & 0.901 & 0.775 & 0.063 & 0.840 & 0.906 & 0.729 & 0.030 & 0.834 & 0.895 & 0.750 & 0.052 \\ ZoomNet & 0.820 & 0.883 & 0.752 & 0.066 & 0.838 & 0.893 & 0.729 & 0.029 & 0.853 & 0.907 & 0.784 & 0.043 \\ DGNet-S & 0.826 & 0.896 & 0.754 & 0.063 & 0.810 & 0.869 & 0.672 & 0.036 & 0.845 & 0.902 & 0.764 & 0.047 \\ DGNet & 0.839 & 0.906 & 0.769 & 0.057 & 0.822 & 0.879 & 0.693 & 0.033 & 0.857 & 0.910 & 0.784 & 0.042 \\ FEDER & 0.807 & 0.873 & 0.785 & 0.069 & 0.823 & 0.900 & 0.740 & 0.032 & 0.846 & 0.905 & **0.817** & 0.045 \\ \hline \multicolumn{13}{c}{**Transformer-Based Method**} \\ \hline COS-T & 0.813 & 0.896 & 0.776 & 0.060 & 0.790 & 0.901 & 0.693 & 0.035 & 0.825 & 0.881 & 0.730 & 0.055 \\ DTINet & 0.857 & 0.912 & 0.796 & 0.050 & 0.824 & 0.893 & 0.695 & 0.034 & 0.863 & 0.915 & 0.792 & 0.041 \\ HitNet & 0.844 & 0.902 & 0.801 & 0.057 & **0.868** & **0.932** & **0.798** & **0.024** & **0.870** & **0.921** & **0.825** & **0.039** \\ FSPNet & 0.856 & 0.899 & 0.799 & 0.050 & **0.851** & 0.895 & 0.735 & **0.026** & **0.879** & **0.915** & 0.816 & **0.035** \\ \hline Ours & **0.870** & **0.924** & **0.828** & **0.047** & **0.879** & **0.938** & **0.806** & **0.021**  
**Effectiveness of FBM. ** From Table 2, (c) uses FBM to fuse the results of method B1 and method B2. Compared with (a) and (b), (c) provides better performance.