# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 5 Experiments
### Comparisons with State-of-the-Art Models  
The performances of our approach and 22 state-of-the-art models on three video datasets are shown in Table 2. Some representative results of our approach are demonstrated in Figure 4. From Table 2, we find that on **Youtube-Objects** and **VOS**, for such larger datasets, our approach obtains the best \(F_{\beta}\) and mIoU scores, while on **SegTrack V2**, our approach ranks the second place (worse than **NLC**). This can be explained by the fact that **SegTrack V2** contains only 14 videos, among which most primary objects have irregular motion patterns. Such videos often perfectly meet the assumption of **NLC** on motion patterns of primary objects, making it the best approach on **SegTrack V2**. However, when the scenarios being processed extend to datasets such as **VOS** that are constructed without such \"constraints\" on motion patterns, the performance of **NLC** drops sharply, as its assumption may sometimes fail (e.g., **VOS** contains many videos only with static primary objects and distractors as well as slow camera motion; see Figure 4). These results further validate that it is quite necessary to conduct comparisons on larger datasets with daily videos (such as **VOS**) so that models with various kinds of assumptions can be fairly evaluated. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**SegTrackV2 (14 Videos)**} & \multicolumn{4}{c}{**Youtube-Objects (127 Videos)**} & \multicolumn{4}{c}{**VOS (200 Videos)**} \\ \cline{2-13}  & **mAP** & **mAR** & \(F_{\beta}\) & **mIoU** & **mAP** & **mAR** & \(F_{\beta}\) & **mIoU** & **mAP** & **mAR** & \(F_{\beta}\) & **mIoU** \\ \hline
**DRFI**[2] & 0.464 & 0.775 & 0.511 & 0.395 & 0.542 & 0.774 & 0.582 & 0.453 & 0.597 & 0.854 & 0.641 & 0.526 \\
**RBD**[34] & 0.380 & 0.709 & 0.426 & 0.305 & 0.519 & 0.707 & 0.553 & 0.403 & 0.652 & 0.779 & 0.677 & 0.532 \\ BL [71] & 0.202 & **0.934** & 0.246 & 0.190 & 0.218 & **0.910** & 0.264 & 0.205 & 0.483 & **0.913** & 0.541 & 0.450 \\  
SP** & 0.789 & 0.778 & 0.787 & 0.669 & **0.778** & 0.820 & **0.787** & **0.675** & 0.805 & 0.910 & **0.827** & 0.747 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of our approach and 22 models. Bold and underline indicate the 1 st and 2 nd performance in each column. Moreover, there exist some approaches (**BL** and **MB+**) on the three datasets that outperform our approach in recall, and some other approaches (**NLC**, **ACO**, **PN+** and **FST**) may achieve better or comparable precision with our approach on **SegTrack V2**. However, the other evaluation scores of the approaches are much worse than our method on the three datasets. That is, none of these approaches simultaneously outperforms our approach in both recall and precision, so our approach often has better overall performance, especially on larger datasets. This may imply that the proposed approach is more balanced than previous works. By analyzing the results on the three datasets, we find that this phenomenon may be caused by the conduction of complementary tasks in CSNet. By propagating both foregroundness and backgroundness, some missing foreground information may be retrieved, while the mistakenly popped-out distractors can be suppressed again, leading to balanced recall and precision. From Table 2, we also find that there exist inherent correlations between salient image object detection and primary video object segmentation. As shown in Figure 4, primary objects are often the most salient ones in many frames, which explains why deep models such as **ELD**, **RFCN** and **DCL** outperform many video-based models such as **NLC**, **SAG** and **GF**. However, there are several key differences between the two problems. First, primary objects may not always be the most salient ones in all frames (as shown in Figure 1). Second, inter-frame correspondences provide additional cues for separating primary objects and distractors, which depict a new way to balance recall and precision.