# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Ablation Study  
We present a comprehensive analysis of the ablation experiments conducted on the DAVIS 2017 validation dataset, focusing on the efficacy of individual modules within our proposed model configuration. Our primary objective is to investigate the impact of each module on model performance, with a particular emphasis on the object-edge extraction and object motion prediction modules introduced in this paper. Unless otherwise specified, our method only undergoes the main training phase during ablation studies. Table 5 summarizes the results obtained from these experiments, revealing critical insights into the contributions of various model components. Notably, our findings demonstrate that both the object-edge extraction module and the object motion prediction module significantly enhance the overall performance of the model. Moreover, the most substantial performance improvement is achieved when both modules are incorporated simultaneously. Table 6 presents the ablation experiments we conducted on the shape spatio-temporal  
Figure 6: Qualitative comparison of our method with baseline methods (STCN [2], AOTT [19], MiVOS [37], RDE [5]). We use the red dotted box to mark where the segmentation method fails. It can be observed that our method, compared to the baseline approach, alleviates the issue of missegmentation of similar objects and achieves higher accuracy in the segmentation of edge regions. constraints module and shape-based refinement module in the object shape extraction module. The experiments indicate that both modules contribute to the overall performance improvement of the model, with the best results achieved when both are used simultaneously. To vividly illustrate the impact of the modules proposed in this article on performance, we present in Figure 1 the influence of these modules on segmentation accuracy. In the first scenario, employing the object shape extraction module results in more accurate segmentation of human edges, especially in regions where two individuals overlap. In the second scenario, utilizing the object motion prediction module significantly improves the segmentation accuracy of the carousel, effectively mitigating object misalignment issues. Figure 7 demonstrates the visual effects of the shape extraction module. It is worth noting that our shape extraction module can precisely capture the edges of the object without being influenced by background edges. This accurate shape information simultaneously enhances the refinement mask accuracy of our shape-based recovery module. In Table 7, we compared the training results of using pretraining, main training, and a combination of both. Importantly, our study found that combining pretraining and main training methods significantly outperformed using either method alone in terms of model performance. Specifically, when conducting only pretraining, we achieved an accuracy of 76.9 \(J\&F\) score on the DAVIS 2017 [13] validation set. With only main training, we obtained an accuracy of 84.2 \(J\&F\) score, as main training aligns more closely with the data on the validation set. However, employing a strategy of pretraining followed by main training resulted in an accuracy of 85.2 \(J\&F\) score, a 1% improvement over the accuracy achieved with main training alone. We believe that pretraining allows our network to learn more generalized segmentation features. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline Without OSEM and OMPM & 82.5 & 79.3 & 85.7 \\ With OSEM & 83.7 & 80.3 & 87.0 \\ With OMPM & 83.9 & 81.3 & 86.5 \\ With OSEM and OMPM & 84.2 & 81.3 & 87.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study. Figure 7: The visualization result of the shape map. It can be seen that our shape extraction module can accurately extract the object shape, and the extracted object shape can further improve the segmentation accuracy.