# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 4 Results
### Comparison with Other Methods  
#### 4.3.1 Quantitative Comparison  
Table 1 shows the comparison of our method on the DAVIS 2017 [13] validation set with other methods. On the DAVIS 2017 [13] validation set we obtained competitive results, achieving a \(J\)&\(F\) score of 85.2. Table 2 shows the comparison of our method with the other method on the YouTube-VOS 2019 [14] validation set, where the performance of our model improves by 0.8% compared to our baseline model [2], obtaining a \(J\)&\(F\) score of 83.5. Table 3 shows the comparison of our method with the other method on the DAVIS 2017 [13] test-dev set, where the performance of our model improves by 1.4% compared to our baseline model [2], obtaining a \(J\)&\(F\) score of 77.5. Table 4 shows the comparison of our method with other methods on the DAVIS 2016 [32] validation set. Although our method performed lower than our baseline method STCN [2] (accuracy lower by 0.2) on the DAVIS 2017 [13] validation set, it significantly surpassed the baseline method [2] on the DAVIS 2017 [13] test-dev set and the YouTube-VOS 2019 [14] validation set (with accuracies higher by 1.4 and 0.8, respectively). It is worth noting that the segmentation difficulty is relatively lower in the DAVIS 2017 [13] validation set compared to the DAVIS  
2017 [13] test-dev set and the YouTube-VOS 2019 [14] validation set. In practice, we found that achieving high accuracy on the DAVIS 2017 [13] test-dev set and the YouTube-VOS 2019 [14] dataset better reflects the generalization performance of our method. For instance, in STCN [2], the authors increased accuracy on the DAVIS 2017 [13] test-dev set (76.1\(\rightarrow\)77.8) and YouTube-VOS 2019 [14] validation set (82.7\(\rightarrow\)84.2) substantially by using additional training data, but the accuracy on the DAVIS 2017 [13] validation set decreased (85.4\(\rightarrow\)85.3).  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Method** & **OL** & **M** & \(\mathcal{J\&\mathcal{F}}\) & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline OSMN [38] & ✓ & & 54.8 & 52.5 & 57.1 \\ RGMR [28] & & & 66.7 & 64.8 & 68.6 \\ RVOS [39] & & & 50.3 & 48.0 & 52.6 \\ Track-Seg [16] & & & 72.3 & 68.6 & 76.0 \\ PreMVOS [40] & ✓ & & 77.8 & 73.9 & 81.7 \\ TVOS [41] & & & 72.3 & 69.9 & 74.7 \\ GC [6] & & ✓ & 71.4 & 69.3 & 73.5 \\ SwiftNet [42] & & ✓ & 81.1 & 78.3 & 83.9 \\ STM [1] & & ✓ & 81.8 & 79.2 & 84.3 \\ GraphMem [43] & ✓ & 82.8 & 80.2 & 85.2 \\ MiVOS [37] & ✓ & 83.3 & 80.6 & 85.9 \\ CFBI [3] & & & 81.9 & 79.1 & 84.6 \\ KMN [9] & ✓ & 82.8 & 80.0 & 85.6 \\ RMNet [8] & ✓ & 83.5 & 81.0 & 86.0 \\ LWL [44] & ✓ & 81.6 & 79.1 & 84.1 \\ CFBI+ [4] & & & 82.9 & 80.1 & 85.7 \\ LCM [7] & ✓ & 83.5 & 80.5 & 86.5 \\ STCN [2] & ✓ & 85.4 & 82.2 & 88.6 \\ RDE [5] & ✓ & 84.2 & 80.8 & 87.5 \\ SCE [45] & ✓ & 84.2 & 80.8 & 87.5 \\ Miles [24] & ✓ & 83.7 & 80.2 & 87.1 \\ InstMove [20] & ✓ & 85.1 & 82.3 & 87.9 \\ Ours & ✓ & 85.2 & 82.3 & 88.0 \\ \hline \end{tabular}  
* OL stands for online fine-tuning-based approach. M stands for memory-based approach. The ✓ symbol represents ”yes”.  
\end{table}
Table 1: Comparison of different methods on the DAVIS 2017 validation set.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Method** & **M** & \(\mathcal{G}\) & \(\mathcal{J}_{S}\) & \(\mathcal{F}_{S}\) & \(\mathcal{J}_{U}\) & \(\mathcal{F}_{U}\) \\ \hline STM [1] & ✓ & 79.2 & 79.6 & 83.6 & 73.0 & 80.6 \\ MiVOS [37] & ✓ & 82.4 & 80.6 & 84.7 & 78.2 & 85.9 \\ CFBI [3] & & 81.0 & 80.6 & 85.1 & 75.2 & 83.0 \\ SST [46] & & 81.8 & 80.9 & - & 76.6 & - \\ STCN [2] & ✓ & 82.7 & 81.1 & 85.4 & 78.2 & 85.9 \\ RDE [5] & ✓ & 81.9 & 81.1 & 85.5 & 76.2 & 84.8 \\ SCE [45] & ✓ & 81.6 & 81.3 & 85.7 & 75.0 & 83.4 \\ Miles [24] & ✓ & 82.3 & 81.6 & 86.0 & 76.3 & 85.2 \\ InstMove [20] & ✓ & 83.4 & 82.5 & 86.9 & 77.9 & 86.0 \\ Ours & ✓ & 83.5 & 82.4 & 87.0 & 78.3 & 86.3 \\ \hline \end{tabular}  
* M stands for memory-based approach. The ✓ symbol represents ”yes”.  
\end{table}
Table 2: Results on the YouTube-VOS 2019 validation set.