# Long-Tail Instance Segmentation Based on Memory Bank and Confidence Calibration
## 4 Experiments
### Main Experiments  
This study analyzed experimental results obtained using the LVIS v0.5 dataset. Several baselines, including Mask R-CNN, the most representative in the field of instance segmentation, were first evaluated. In addition, we evaluated RFS, which uses only the resampling method and is based on the Mask R-CNN, and EQL, which only has some changes to the cross-entropy loss function of the Mask R-CNN. Finally, we compared the final results with the results obtained by a combination of the above two methods. The results are presented in Table 3. Experiments show that compared with the baseline Mask R-CNN with ResNet50 as the backbone, the method proposed in this paper has a 5% accuracy gain. It obtains an accuracy gain of 5.5% using ResNet101 as a backbone. The AP\({}_{\text{m}}\) is improved by 0.8% and 1.9%, respectively, when compared with EQL+RFS on both backbone networks. In addition, a good long-tail model must rank true positives higher so that they can be included in the cap, as shown in Figure 7. Our model performs much better than the baseline. In addition, we focus on the comparison of the AP\({}_{r}\) of the proposed method with that of the Mask R-CNN baseline. The index is significantly improved by 1.9% and 3.3% respectively, which shows the effectiveness of this method when applied to long-tail data, and it effectively solves the problem of long-tail distribution. This suggests that the combination of a memory-bank-centered resampling approach with a confidence post-processing calibration can work well to make the model pay attention to tail class objects and perform well to detect segmentation. A visualization of the experimental results is shown in Figure 8. EQL+RFS has problems with incorrect recognition and inaccurate mask edges. For example, only our model identifies an instance of \"bulletin_board\" in the first group. In the second group, our model identifies a \"monitor\" class object with the clearest mask edge, whereas the other models incorrectly identify it as a \"laptop_monitor\" with an incorrect mask region. Our model also successfully identifies the \"vent\" class object in the third group and successfully identifies the tail class \"ball_cap\", which was not identified by the first two methods. In the fourth group, it successfully identifies \"helmet\" and \"glove\", which were not identified by the first two methods. In the fifth group, it successfully identifies \"shoe\" and \"sandal\", and in the sixth group, it successfully identifies the \"banana\" with clear mask edges. Figure 6: Effect of DCT on training loss. These results show that the proposed method effectively detects and segments the tail class data. However, as shown in Figure 9, since our model cannot sufficiently extract all the targets in the dataset for the tail class when extracting them from the memory, only the targets within a certain batch can be extracted. If all targets of that class in the dataset are extracted, it will lead to an oversized and inefficient model. Therefore, there are still some cases of misidentified or unidentified recognition. The proposed method was also compared with other advanced models using the same comparison metrics as above. First, it was compared with two-stage decoupled training methods, and the results are shown in Table 4. The experimental results reveal that both methods further improve the overall level of accuracy, mainly by enhancing the tail class segmentation accuracy. Of the comparison methods, the LWS method achieves limited improvements. This is because the model only learns a scaling factor to adjust the decision boundary of the classifier without changing the classifier itself. Furthermore, compared with BAGS, the proposed method achieves the same accuracy using the ResNet50 backbone. With the ResNet101 and X101 backbones, our method achieves higher accuracy than the BAGS and ResNet50 results by 1.4% and 0.7%, respectively. Moreover, the proposed model is trained jointly with the classifier, and no additional fine-tuning stage is required.