# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 5 Conclusions  
In this paper, we first systematically analyzed the category imbalance problem in long-tail datasets and the inappropriateness of conventional deep-learning methods for long-tail datasets, noting that the segmentation task is particularly significant. To address the difficulties of long-tail instance segmentation tasks, this paper proposed a dynamic memory bank for tail classes to perform object-level resampling of tail class objects in images. Furthermore, a post-processing calibration method was proposed to solve the problem of imbalances in the number of targets due to the discrete number of targets in the training process of the dynamic memory bank. Finally, to compensate for the low accuracy of the mask edge, a DCT binary mask was used to further improve the mask quality.  
The effectiveness of the proposed model under different settings is demonstrated by ablation experiments. However, the low accuracy improvement cannot match the high time consumption due to the model training efficiency, which is a challenge that needs to be addressed in the future. However, the experimental results show that the proposed  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Backbone** & **AP\({}_{\text{bbox}}\)** & **AP\({}_{\text{r}}\)** & **AP\({}_{\text{c}}\)** & **AP\({}_{\text{f}}\)** & **AP\({}_{\text{segm}}\)** \\ \hline Mask & & 20.8 & 3.1 & 19.4 & 29.8 & 21.2 \\ R-CNN & & - & - & - & - & 23.0 \\ LST & & - & 14.4 & 24.4 & 26.8 & 24.1 \\ LWS & & 23.6 & 8.5 & 23.9 & 29.3 & 24.0 \\ EQL & Res50 & - & 14.5 & 24.3 & 28.4 & 24.4 \\ Forest & & 25.9 & 18.3 & 26.4 & 27.6 & 25.6 \\ R-CNN & & 25.8 & 18.0 & 26.9 & 28.7 & 26.3 \\ BAGS & & 25.8 & 17.9 & 24.8 & 30.1 & 26.9 \\ Ours & & 25.8 & 17.9 & 24.8 & 30.1 & 26.9 \\ \hline Mask & & 22.3 & 4.3 & 22.7 & 30.2 & - \\ R-CNN & & 26.4 & 16.8 & 25.8 & 30.9 & - \\ ACSL & 27.3 & 19.3 & 27.6 & 30.7 & 27.5 \\ Forest & & 27.5 & 20.1 & 27.9 & 28.3 & 26.9 \\ R-CNN & & 27.8 & 19.2 & 27.3 & 31.9 & 29.3 \\ \hline BAGS & & 27.8 & 18.8 & 27.3 & 32.1 & - \\ Forest & X101 & 28.8 & 20.6 & 29.2 & 31.7 & 28.5 \\ R-CNN & & 29.2 & 19.5 & 29.0 & 33.5 & 30.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with state-of-the-art methods on the LVIS v0.5 validation set.  
method effectively solves the problem of long-tailed instance segmentation and effectively improves the quality of mask generation by obtaining an accuracy 2.2% higher than that of the baseline method.  
Conceptualization, X.F. and W.P.; methodology, X.F. and T.L. ([PERSON]); software, T.L. ([PERSON]) and T.L. ([PERSON]); validation, X.F. and T.L. ([PERSON]); formal analysis, X.F. and H.B.; investigation, X.F. and T.L. ([PERSON]); resources, X.F., T.L. ([PERSON]), W.P. and H.B.; data curation, T.L. ([PERSON]); writing--original draft preparation, X.F.; writing--review and editing, X.F. and W.P.; visualization, T.L. ([PERSON]) and H.L.; supervision, W.P. and H.B.; project administration, W.P.; funding acquisition, H.B. and W.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 61802019, 61932012, 61871039, 61906017 and 62006020), the Academic Research Projects of Beijing Union University (No. ZK10202202), the Beijing Municipal Education Commission Science and Technology Program (Nos. KM201911417003, KM201911417009 and KM201911417001), the Beijing Union University Research and Innovation Projects for Postgraduates (No.YZ2020K001), and the Premium Funding Project for Academic Human Resources Development in Beijing Union University under Grant BPHR2020 DZ02.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.