# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Comparison with Other Methods
#### 4.3.2 Qualitative Comparison  
Figure 6 compares the segmentation results of our method and the baseline method in similar object scenes. We have compared our method with the state-of-the-art methods, including STCN [2], AOTT [19], RDE [5], and MiVOS [37]. STCN serves as both a memory-based method and our baseline approach. AOTT [19] is a representative method based on Transformer, while RDE [5] is a typical approach that occupies fixed-size memory. MiVOS [37] is an improved version of the milestone method STM [1] within the category of memory-based approaches. From Figure 6, it is clearly evident that our method outperforms other techniques in terms of segmentation accuracy, particularly when dealing with similar objects or handling fine details at the boundaries of overlapping objects. Specifically, MiVOS [37] and STCN [2] erroneously segmented the car on the left in the background as the object in the first scene, and incorrectly segmented the standing person on the left in the second scene. In contrast, our method excelled in both of these scenarios. [PERSON] [19] and [PERSON] [2] mistakenly segmented the background horse and person as the object in the third scene. In the fourth scene, [PERSON] [19] failed in the segmentation of the deer head on the right, indicating inadequate precision in object-edge handling. Although [PERSON] [2] performed  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **OL** & **M** & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline OSMN [38] & ✓ & & 41.3 & 37.3 & 44.9 \\ OnAVOS [47] & ✓ & & 56.5 & 53.4 & 59.6 \\ FEELVOS [48] & & & 57.8 & 55.2 & 60.5 \\ PreMVOS [40] & ✓ & & 71.6 & 67.5 & 75.7 \\ STM [1] & & ✓ & 72.2 & 69.3 & 75.2 \\ RMNet [8] & & ✓ & 75.0 & 71. & 78.1 \\ CFBI [3] & & 76.6 & 73.0 & 80.1 \\ KMN [9] & ✓ & 77.2 & 74.1 & 80.3 \\ CFBI+ [4] & & & 78.0 & 74.4 & 81.6 \\ LCM [7] & ✓ & 78.1 & 74.4 & 81.8 \\ STCN [2] & ✓ & 76.1 & 72.6 & 79.6 \\ Ours & ✓ & 77.5 & 74.0 & 81.0 \\ \hline \hline \end{tabular}  
* \(\overline{\text{OL}}\) stands for online fine-tuning-based approach. M stands for memory-based approach.The ✓ symbol represents ”yes”.  
\end{table}
Table 3: Results on the DAVIS 2017 test-dev set.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Method** & **OL** & **M** & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline OSMN [38] & ✓ & & 41.3 & 37.3 & 44.9 \\ OnAVOS [47] & ✓ & & 56.5 & 53.4 & 59.6 \\ FEELVOS [48] & & & 57.8 & 55.2 & 60.5 \\ PreMVOS [40] & ✓ & & 71.6 & 67.5 & 75.7 \\ STM [1] & & ✓ & 72.2 & 69.3 & 75.2 \\ RMNet [8] & & ✓ & 75.0 & 71. & 78.1 \\ CFBI [3] & & & 76.6 & 73.0 & 80.1 \\ KMN [9] & ✓ & & 77.2 & 74.1 & 80.3 \\ CFBI + [4] & & & 78.0 & 74.4 & 81.6 \\ LCM [7] & ✓ & 78.1 & 74.4 & 81.8 \\ STCN [2] & ✓ & 76.1 & 72.6 & 79.6 \\ Ours & ✓ & 77.5 & 74.0 & 81.0 \\ \hline \hline \end{tabular}  
* \(\overline{\text{OL}}\) stands for online fine-tuning-based approach. M stands for memory-based approach.The ✓ symbol represents ”yes”.  
\end{table}
Table 4: Results on the DAVIS 2016 validation set.  
well in object-edge-region processing, segmentation failure occurred due to the complete confusion of two objects. On the other hand, our approach, aided by the object motion prediction module and object shape extraction module, not only accurately segments the edge regions of the object but also exhibits strong robustness against challenges such as occlusion. In the fifth and final scenes, RDE [5] and STCN [2] suffer from background-foreground confusion in the object-edge regions and object confusion errors. In contrast, our method, leveraging shape information effectively, achieves higher segmentation accuracy.