# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments
### Comparison with State-of-the-Arts  
To demonstrate the effectiveness of our CDNet, we compare it with 28 state-of-the-art methods, including CNN-based methods (i.e., SINet [15], C2 FNet [20], TINet [39], JSCOD [40], PFNet [21], LSR [16], R-MGL [41], S-MGL [41], UGTR [42], NCHIT [43], ERNRet [44], TPRNet [45], FAPNet [46], BSANet [47], OCENet [48], PreyNet [49], SINetV2 [18], BGNet [19], SeMaR [50], FDNet [51], ZoomNet [27], DGNet-S [52], DGNet [52], and FEDER [22]) and Transformer-based methods (i.e., COS-T [23], DTINet [24], HitNet [25], and FSPNet [26]). To ensure a fair comparison, the prediction maps used in the evaluation of the above methods were either provided by the authors or generated by retraining the models using open-source code. Additionally, a consistent evaluation approach is applied to all prediction maps, ensuring a standardized comparison. **Quantitative Comparison. ** Table 1 presents a comprehensive summary of the quantitative results obtained by our proposed method when compared to 28 competing approaches. The evaluation was conducted on three challenging COS benchmark datasets, and the performance was measured using four evaluation metrics. It can be seen that the Transformer-based methods generally outperform the CNN-based methods. Furthermore, our proposed method consistently surpasses all other models on evaluated datasets. Compared to the recently proposed state-of-the-art HitNet [25], our method achieves average performance gains of 1.80%, 1.43%, 1.97%, and 0.73% on average in terms of \(S_{\alpha}\), \(E_{\phi}^{ad}\), \(F_{\beta}^{\psi}\) and \(M\) on these three datasets, respectively. Compared to the recently proposed FPNet [26], our method achieves average performance gains of 1.67%, 2.97%, 4.43%, and 0.43%, respectively. Besides, compared to the recently proposed CNN-based methods (i.e., FEDER [22] and DGNet [52]), our method shows significant performance improvements of 5.33%, 4.00%, 4.70%, and 1.60% over FEDER and 3.93%, 3.43%, 7.90%, and 1.13% over DGNet on average for \(S_{\alpha}\), \(E_{\phi}^{ad}\), \(F_{\beta}^{\psi}\) and \(M\), respectively. The superiority in performance benefits from the compensation of the effective fusion of local features from the CNN backbone and global features from the transformer backbone, and the smooth and curiosity-refinement module to accumulate more subtle clues of the camouflage objects. In addition to the comprehensive quantitative comparisons, we also provide PR curves and F-measure curves in Figure 4. The results undeniably demonstrate that our model surpasses other COS methods and attains the highest performance. **Qualitative Comparison. ** Figure 5 shows the segmentation results of our proposed method with ten representative COS competitors in challenging scenarios. The visual results presented in Figure 5 demonstrate that our method outperforms the competitors by effectively accurately segmenting camouflaged objects with higher precision and completeness. Specifically, in the 1 st and 2 nd rows, it can be observed that our method can completely segment large camouflaged objects while some methods fail to locate them. In the 3 rd and 4 th rows, it can be observed that our method can more precisely segment small camouflaged objects than other methods. In the 5 th and 6 th rows, the segmentation of multiple camouflaged objects presents a significant challenge. It can be observed that our method can effectively segment multiple camouflaged objects while other methods suffer from inaccurate segmentation results. In the 7 th and 8 th rows, it can be noticed that our method can completely segment occluded objects while some methods fail to segment them completely. In the 9 th and 10 th rows, the boundary between the object and the background is not sharp, which brings a serious challenge to identifying them from a similar background. In this case, our method still exhibits superior performance by accurately segmenting camouflaged objects with rich details.