# Camouflaged Object Detection That Does Not Require Additional Priors
## 4 Experiments and Analysis
### Ablation Study
When \(\lambda\) = 0.5, the \(S_{\alpha}\) metric increases by 0.1%, the \(E_{\phi}\) metric increases by 0.3%, and the \(F_{\beta}^{w}\) metric increases by 0.2% compared to the predicted value when \(\lambda\) = 0.4 (i.e., the second-highest value in the test). Based on these results, we have set the value of \(\lambda\) to 0.5 in this experiment. **AFFM fusion feature layer experiment. ** In generating camouflage cues in AFFM, experiments were conducted to ascertain which feature layer information should be combined adaptively. As demonstrated in Table 4, the model's detection performance exhibited a significant improvement by including more feature layers in the fusion process. The most favorable detection outcomes were achieved when all feature layers were employed for adaptive fusion. These findings underscore the essential role played by each feature layer in augmenting the model's comprehension of camouflaged objects. It is crucial to emphasize that the contribution of each layer to the final test result is distinct and irreplaceable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Layers**} & \multicolumn{2}{c}{**CAMO-Test**} & \multicolumn{2}{c}{**COD10K-Test**} & \multicolumn{2}{c}{**NC4K**} \\  & & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) \\ \hline
**1** & \(f_{4}\) & 0.822 0.864 0.745 0.074 & 0.827 0.871 0.694 0.035 & 0.855 0.892 0.773 0.047 \\
**2** & \(f_{4}\),\(f_{1}\) & **0.827** 0.870 0.752 0.072 & 0.830 0.877 0.702 0.034 & **0.859** 0.899 0.783 0.045 \\
**3** & \(f_{4}\),\(f_{3}\),\(f_{1}\) & 0.819 0.866 0.747 0.073 & 0.831 0.878 0.706 **0.032** & 0.857 0.896 0.782 **0.042** \\
**4** (Ours) & \(f_{4}\),\(f_{3}\),\(f_{2}\),\(f_{1}\) & **0.827** **0.877** **0.754** **0.069** & **0.833** **0.888** **0.710** 0.033 & **0.859** **0.902** **0.785** 0.045 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AFFM adaptive feature fusion experiment. The best results are highlighted in **bold**. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CAMO-Test**} & \multicolumn{2}{c}{**COD10K-Test**} & \multicolumn{2}{c}{**NC4K**} \\  & & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) & \(S_{\bullet}\uparrow E_{\phi}\uparrow F_{\beta}^{w}\uparrow\mathcal{M}\downarrow\) \\ \hline
**1** & Base & 0.799 0.860 0.717 0.078 & 0.801 0.867 0.655 0.038 & 0.834 0.887 0.745 0.050 \\
**2** & Base+ERM & 0.814 0.852 0.727 0.078 & 0.827 0.875 0.690 0.034 & 0.854 0.892 0.771 0.046 \\
**3** & Base+AFFM+FSM & 0.818 0.870 0.746 0.072 & 0.832 0.885 0.710 **0.032** & 0.852 **0.903** 0.782 0.045 \\
**4** & Base+FSM+ERM & 0.822 0.864 0.745 0.074 & 0.827 0.871 0.694 0.035 & 0.855 0.892 0.773 0.047 \\
**5** (ours) & Base+AFFM+FSM+ERM & **0.827** **0.877** **0.754** **0.069** & **0.833** **0.888** **0.710** 0.033 & **0.859** 0.902 **0.785** **0.045** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation for ablation studies on three datasets. The best results are highlighted in **bold**.