# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 4 Experiments
### Ablation Experiment  
In this study, ablation experiments were conducted for each method, using the indicators described above and shown in Table 2. First, we validated the post-processing confidence method. Using a ResNet50 backbone, the method proposed in this paper achieves an \(\text{AP}_{\text{bbox}}\) and \(\text{AP}_{\text{r}}\) that is higher than those of the Mask R-CNN baseline model by 23.6% and 9.1%, respectively. With a ResNet101 backbone, \(\text{AP}_{\text{bbox}}\) is improved by 2.9% and \(\text{AP}_{\text{r}}\) is improved by 9.8%. These results show that the post-processing confidence method proposed in this paper improves the prediction scores of tail and common classes by changing the confidence levels of the different classes; this effectively solves the instance segmentation problem of long-tail datasets.  
Table 2 presents the effect of using a dynamic memory bank. The results show that the accuracy of the model is greatly improved in the tail and common classes. With a ResNet50 backbone, the model achieves an improvement \(\text{AP}_{\text{r}}\) of 14.1% and an improvement \(\text{AP}_{\text{c}}\) of 5.7% compared with the baseline. These results illustrate that the dynamic memory bank effectively resamples the tail classes of objects by substantially increasing the \(\text{AP}_{\text{r}}\) and \(\text{AP}_{\text{c}}\) as well as increasing the \(\text{AP}_{\text{bbox}}\).  
Finally, we analyze the effectiveness of the combined methods. Using a ResNet50 backbone, the proposed method yields \(\text{AP}_{\text{bbox}}\), \(\text{AP}_{\text{r}}\), and \(\text{AP}_{\text{c}}\) results that are 5.1%, 12.6%, and 6.7% better, respectively, than those of the baseline. However, compared with the results of the method with the dynamic memory bank, the \(\text{AP}_{\text{r}}\) results are slightly lower. The reason for this is that the dynamic memory bank increases the frequency of the tail classes, which leads to an excessive reduction in the confidence level of some of the rare classes at a later stage. However, the substantial improvement of the other three indicators, especially \(\text{AP}_{\text{c}}\), also indicates the effectiveness and relevance of the method.  
In addition, we evaluated the effectiveness of the DCT by plotting the gradient descent curve. As shown in Figure 6, both models were trained on different backbone networks  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss** & **Weight** & **LVIS AP** \\ \hline  & 0.005 & 39.3 \\ \(l_{1}\) & 0.006 & 39.4 \\  & 0.007 & 39.6 \\  & 0.008 & 39.2 \\ \hline  & 0.004 & 39.1 \\ smooth \(l_{1}\) & 0.005 & 39.2 \\  & 0.006 & 39.5 \\  & 0.007 & 39.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of loss with different weights.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Res50** & **Res101** & **Post-Processing Calibration** & **Memory Bank** & \(\text{AP}_{\text{bbox}}\) & \(\text{AP}_{\text{r}}\) & \(\text{AP}_{\text{c}}\) & \(\text{AP}_{\text{f}}\) \\ \hline Mask R-CNN & \(\surd\) & & \(\times\) & \(\times\) & 20.8 & 3.1 & 19.4 & 29.8 \\ Ours method & \(\surd\) & & \(\surd\) & \(\times\) & 23.6 & 12.2 & 22.7 & 29.3 \\ Ours method & \(\surd\) & & \(\times\) & \(\surd\) & 25.6 & 17.2 & 25.1 & 29.8 \\ Ours method & \(\surd\) & & \(\surd\) & \(\surd\) & 25.9 & 15.7 & 26.1 & 30.4 \\ Mask R-CNN & & \(\surd\) & \(\times\) & \(\times\) & 22.3 & 3.8 & 20.9 & 30.8 \\ Ours method & & \(\surd\) & \(\surd\) & \(\times\) & 25.2 & 13.6 & 24.4 & 31 \\ Ours method & & \(\surd\) & \(\times\) & \(\surd\) & 27.3 & 19.1 & 26.8 & 31.2 \\ Ours method & & \(\surd\) & \(\surd\) & \(\surd\) & 27.5 & 18.4 & 27.9 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments.  
and both converged. However, the training curve of the model with DCT is smoother and less volatile.