# CenterInst: Center-Based Real-Time Instance Segmentation
## 4 Experimental Results
### Ablation Studies  
We analyzed the performance of CenterInst through a series of ablation experiments. Initially, we validated the effectiveness of the key components of the three proposals: the center-guided query selector, the center-based query decoder, and the lightweight structure. Subsequently, we explored the impact of other designs on CenterInst. Unless otherwise specified, we conducted the experiments using the ResNet-50 backbone for CenterInst with a lightweight design and without the contrastive denoising training strategy. All ablation results were evaluated on the COCO val2017 dataset. Center-guided query selector: As shown in Table 2, we conducted comparative experiments between the segmentation-guided query selector in FastInst and our proposed center-guided query selector. The results indicated that the center-guided query selector exhibited superior performance while incurring almost no additional computational cost or inference time. This suggests that query proposals obtained based on the center position \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Epoch** & **Size** & **AP\({}_{\text{test}}\)** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{5}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **FLOPS** & **FPS** \\ \hline SOLOv2 [2] & R50 & 36 & 448 & 34.0 & 54.0 & 36.1 & 12.9 & 34.7 & 48.7 & – & – \\ SparseInst [3] & R50 & 144 & 608 & 34.7 & 55.3 & 36.6 & 14.3 & 36.2 & 50.7 & – & – \\ YOLACT [14] & R50 & 54 & 550 & 28.2 & 46.4 & 29.2 & 9.2 & 29.3 & 44.8 & – & – \\ CondInst [13] & R50 & 36 & 800 & 37.8 & 59.1 & 40.5 & **21.0** & 40.3 & 48.7 & – & – \\ FastInst [4] & R50 & 50 & 640 & 38.6 & 60.2 & 40.6 & 10.8 & **56.2** & **75.2** & 75.5 & 50.3 \\ CenterInst & R50 & 36 & 640 & 39.2 & 60.9 & 41.6 & 17.7 & 41.5 & 56.7 & 69.1 & 54.1 \\ CenterInst-large & R50 & 36 & 640 & **39.5** & **61.3** & **41.8** & 18.0 & 41.9 & 57.3 & 84.9 & 45.5 \\ \hline SparseInst & R50-d-DCN & 144 & 608 & 37.9 & 59.2 & 40.2 & 15.7 & 39.4 & 56.9 & – & – \\ FastInst & R50-d-DCN & 50 & 640 & 40.5 & 62.6 & 42.9 & 10.0 & **54.9** & **74.5** & 77.9 & 46.9 \\ CenterInst & R50-d-DCN & 36 & 640 & **40.9** & **63.1** & **43.4** & **18.9** & 43.4 & 59.8 & 71.5 & 48.3 \\ \hline \hline \end{tabular}  
* The “Epoch” column indicates the total number of epochs required during training, the “Size” column represents the dimensions of the input images during inference, and “FLOPs” denotes the floating point operations performed by the model during inference on input images. Bold data indicates that this method outperforms others in this metric.  
\end{table} 
Table 1: Performance of instance segmentation on COCO test-dev. We compared CenterInst with state-of-the-art methods, and it outperformed most previous real-time instance segmentation methods in both inference speed and accuracy. Notably, CenterInst surpassed the previous leading method, FastInst. are more accurate and representative, emphasizing the effectiveness of relying on the center position. Center-guided sampling query decoder: As shown in Table 3, our center-based query decoder with local sampling outperformed the global sampling query decoder. This indicates that local sampling based on the instance center position can effectively extract features from the feature map, reducing the introduction of irrelevant region features, thereby achieving a similar effect to the masked attention used in FastInst. The introduction of multiscale features further enhanced the model's performance. Lightweight inference structure: Table 4 demonstrates the performance of CenterInst and FastInst, each with the same lightweight design. FastInst experienced a performance decrease of 1.3 AP when employing the lightweight structure, whereas CenterInst showed a modest decrease of only 0.3 AP. This further emphasizes that CenterInst effectively leverages information from multiscale feature maps using the center-guided local sampling query decoder module, without relying too heavily on more refined feature maps.