# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Ablation Study
Figure 8 shows the visual display of the effectiveness of the spatio-temporal constraints module, in which \"Input\" denotes the input image, \"Mask T-1\" denotes the object mask of frame T-1, \"Before\" denotes the shape information before the spatial restriction module is processed, and \"After\" denotes the shape information after the processing. It can be seen that after the spatio-temporal constraints module is processed, it can effectively alleviate the background interference of the object shape. A visual example of the shape-based refinement module's effects is shown in Figure 9. In this figure, \"Input\" denotes the input image, \"Shape\" denotes the object shape, \"Before\" denotes the mask before processing through the shape-based refinement module, and \"After\" denotes the mask after processing. The figure illustrates that by leveraging shape information effectively, our shape-based refinement module can significantly improve the segmentation accuracy at the edges of the object. A comparison between the intermediate layer features of our approach and STCN [2] is presented in Figure 10. The points in the figure represent objects in a single frame of the video, with different colors indicating different objects. The first row shows the salsa video from DAVIS 2017 test-dev, featuring 10 similar objects and facing challenges such as occlusion and rapid movement, making segmentation difficult. The second row displays the sun-people video from DAVIS 2017 [13] test-dev, including four similar objects. Observing both scenarios, compared to STCN [2], our method's extracted intermediate features exhibit larger feature distances between different objects, demonstrating higher discriminability for similar objects and effectively overcoming challenges such as occlusion and rapid movement. Figure 11 illustrates a comparison between the heatmap generated by our method and the baseline method STCN [2]. As shown in the figure, it can be observed that our method is less affected by background interference compared to the baseline STCN [2], focusing more on the object. Specifically, in the first and second scenarios, the attention of our baseline method is dispersed to irrelevant background, impacting its segmentation accuracy to some extent, whereas our method is less affected by background interference. In the second scenario, our baseline method [2] concentrates attention only on part of the object, while our method's attention covers a broader region, resulting in higher segmentation accuracy, especially at the edges of the object. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline With shape spatio-temporal constraints & 83.4 & 80.3 & 86.4 \\ With shape-based refinement module & 83.5 & 80.1 & 86.8 \\ With both & 83.7 & 80.3 & 87.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation study of object shape extraction module. Figure 8: The figure illustrates our ablation experiments on the spatio-temporal constraints module. It can be observed that the processing of the spatio-temporal constraints module effectively alleviates the interference of the background on the shape of the object. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline Pretraining only & 76.9 & 74.4 & 79.5 \\ Main training only & 84.2 & 81.3 & 87.1 \\ Both & 85.2 & 82.3 & 88.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effects of pretraining on static images/main training on the DAVIS 2017 [13] validation set. Figure 11: A comparison of the heatmap generated by our method with STCN [2]. As shown in the figure, it can be observed that our method pays more attention to the object region compared to STCN [2]. Figure 10: Comparison between the middle layer features of our method and the STCN [2]. The points in the figure represent individual frames in the video, and different colors indicate different objects.