# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 5 Experiments
### Detailed Performance Analysis
#### 5.3.2 Effectiveness of Neighborhood Reversible Flow  
Through the above complementary network branches, the salient foreground and background maps in the intra-frame are well obtained, while the initialization operation cannot ensure the temporal consistency of segmented objects, e.g., the initialized predictions by CSNet outperform the ones by the CCNN in term of mAP, mAR, \(F_{\beta}\) and mIoU but become inferior in temporal stability mT. Thus, the thought of improving the temporal relationship was proposed in Section 4, i.e., finding and propagating the reliable inter-frame correspondences by applying the neighborhood reversible flow to make the consistent salient subsets enhanced and accidental distractors suppressed. Consequently, the final primary video objects with spatiotemporal consistency are yielded. **Effectiveness of Neighborhood Reversible Flow**. To prove this thought, we compare the initialized results by CCNN (CSNet) with the optimized results (V-Init.(R-Init.) + NRF) by neighborhood reversible flow. As shown in Table 3, the temporal stability measure mT of optimization results in CCNN (CSNet) cases decrease from 0.121 (0.122) to 0.109 (0.108) compared with the initialized predictions (V-Init. (FG + BK), R-Init. (FGp + BKp)). At the same time, the other evaluation scores are also improved, e.g., the mIoU score increases from 0.689 to 0.710. The superiority will become more obvious if we directly compare V-Init. + NRF with V-Init. FG, i.e., conduct the fusion operation on foreground and background in the process of neighborhood reversible flow just like we really do. This means that by propagating the neighborhood reversible flow, the spatial subsets of primary objects in intra-frame can be refined from a temporal perspective, and the inter-frame temporal consistency can be enhanced. Finally, the primary video objects with favourable spatiotemporal consistency can pop out. As shown in Figure 6, the primary objects in most video frames are initialized as the horce, while the objects that only lasts for a short while are mistakenly classified into foreground due to their spatial saliency in certain frames. Fortunately, the distractors are well suppressed by the optimization of neighborhood reversible flow (see the third rows in Figure 6). Thus, via propagating salient cues in inter-frames, background objects could be effectively suppressed, only preserving the real primary one. To further demonstrate the effectiveness of neighborhood reversible flow, we tested our approach with two new settings based on the CCNN. In the first setting, we replaced the correspondence from Equation (7) with the cosine similarity between superpixels. In this case, the \(F_{\beta}\) and mIoU scores of our approach on **VOS** drop to 0.795 and 0.696, respectively. Such performance is still better than the initialized foreground maps but worse than the performance when using the neighborhood reversible flow (\(F_{\beta}\) = 0.806, mIoU = 0.710). This result indicates the effectiveness of neighborhood reversibility in temporal propagation. In the second setting, we set \(\lambda_{c}=+\infty\) in Equation (10), implying that primary objects in a frame are solely determined by the foreground and background propagated from other frames. When the spatial predictions of each frame are actually ignored in the optimization process, the \(F_{\beta}\) (mIoU) scores of our approach on **VOS** only decrease from 0.806 (0.710) to 0.790 (0.693), respectively. This result proves that the inter-frame correspondences encodedin the neighborhood reversible flow are quite reliable for efficient and accurate propagation along the temporal dimension. It is worth mentioning that in the previous initialization process, the predictions are all pixel-wise, while the temporal optimization via neighborhood reversible flow is conducted on superpixel wise foreground/background maps in order to reduce time consumption, i.e., the predictions need to be converted from the pixel to superpixel and finally converted to pixel.