# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 5 Experiments
### Ablation Study
#### 5.3.3 Component-Wise Analysis  
We next investigate the effects of the Temporal Relation Module (TRM) and Sketch-Anchored Temporal Contrastive Learning (STCL) method. Table 7 shows the results of various configurations. Row #1 presents the performance when only the sketch-video fusion module is used. Rows #2 and #3 show the results of adding STCL and TRM individually, respectively. Both additions lead to significant improvements over Row #1 on both datasets, highlighting the effectiveness of each module. Row #4 presents our full design, which incorporates both modules and achieves the best performance, demonstrating that the two components work synergistically and confirming the overall effectiveness of our approach.  
In Figure 11, we provide a visual comparison to demonstrate the effectiveness of TRM and STCL. The figure illustrates segmentation results on video sequences containing multiple similar objects. The first, third, and fifth rows show results without these modules, while the second, fourth, and sixth rows display results with TRM and STCL integrated. Without the proposed modules, the segmentation exhibits several limitations: (1) unclear segmentation of fine details, such as the legs of the giraffe; (2) partial loss of the main object as seen in the last frame of the first row; and (3) the misclassification of objects from other categories as the target object. For instance, in the first frame of the fifth row, the fence posts are mistakenly segmented as the giraffe due to their visual similarity. By incorporating TRM and STCL, these issues are effectively alleviated, indicating that the network learns more discriminative and temporally consistent features. These results underscore the efficacy of the proposed modules in enhancing segmentation accuracy and robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Level** & \(\mathcal{J}\)**\&\(\mathcal{F}\)** & \(\mathcal{J}\) & \(\mathcal{F}\) \\ \hline \multirow{2}{*}{Add} & res5 & 74.8 & 72.8 & 76.8 \\  & multi-level & 75.1 & 73.2 & 77.1 \\ \hline \multirow{2}{*}{Mul} & res5 & 74.8 & 72.8 & 76.8 \\  & multi-level & 74.9 & 72.9 & 77.0 \\ \hline \multirow{2}{*}{Concat} & res5 & 75.4 & 73.4 & 77.5 \\  & multi-level & 75.1 & 73.0 & 77.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on design of function (Equation (10)) for Cross-Q on the Sketch-YouTube-VOS validation set. Add indicates element-wise add, Mul indicates element-wise multiplication, and Concat indicates concatenate. Level of res5 means using res5-sketch + res5-visual features while multi-level means using multi-level-visual + res5-sketch features.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **TRM** & **STCL** & \multicolumn{2}{c}{**Sketch-Youtube-VOS\({}_{val}\)**} & \multicolumn{2}{c}{**Sketch-DAVIS17\({}_{val}\)**} \\  & **Section 4.5** & **Section 4.6** & \(\mathcal{J}\)**\&\(\mathcal{F}\)** & \(\mathcal{J}\)** & \(\mathcal{F}\)** & \(\mathcal{J}\)**\& \(\mathcal{F}\)** \\ \hline \#1 & & & & 75.4 & 73.4 & 77.5 & 70.2 & 66.9 & 73.4 \\ \#2 & & & & 76.2 & 74.2 & 78.3 & 71.4 & 67.7 & 74.9 \\ \#3 & & & & 76.8 & 73.9 & 79.7 & 7 We further demonstrate the influence of the search radius in Equation (11). As shown in Table 8, #1 represents the results with a search radius of \(R=1\), equivalent to a baseline that applies attention only between the keys and queries in adjacent frames. And #2 and #3 present the results with gradually increased search radii of \(R=3\) and \(R=5\), respectively. A comprehensive comparison of these rows reveals that performance improves progressively as the search radius increases. This indicates that incorporating hierarchical features through varying kernel sizes enhances segmentation tasks, particularly for datasets containing objects of varying scales.