# CenterInst: Center-Based Real-Time Instance Segmentation
## 4 Experimental Results
### Ablation Studies
As a result, CenterInst efficiently reduces model computation and inference time by minimizing pixel update frequency, thereby accelerating inference speed while maintaining accuracy. Query proposal: As shown in Table 5, for the center-guided query selector, we first replaced the high-resolution feature maps \(E3\) with lower-resolution feature maps \(E4\) for query acquisition, leading to a decrease in the model's predictive capabilities for small objects. Subsequently, we stopped obtaining queries using bilinear interpolation on the coordinates of the center point, resulting in an overall accuracy drop. This indicates that \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **LIS** & \(\mathbf{AP_{val}}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline FastInst & ✓ & 36.1 (\(-1.3\)) & 57.0 & 37.9 & 14.3 & 38.7 & 58.4 \\ FastInst & & 37.4 & 58.3 & 39.2 & 15.2 & 40.0 & 59.2 \\ \hline CenterInst & ✓ & 37.8 (\(-0.3\)) & 59.5 & 39.5 & 16.9 & 40.9 & 59.5 \\ CenterInst-large & & 38.1 & 59.8 & 39.9 & 16.3 & 41.2 & 60.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lightweight inference structure. Comparison of the performance of FastInst and CenterInst when employing lightweight inference structures. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Query Selector** & \(\mathbf{AP_{val}}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) & \(\mathbf{FLOP_{S}}\) & **FPS** \\ \hline Seg-guided & 37.3 & 59.0 & 38.9 & 16.5 & 40.5 & 59.1 & 69.0 G & 54.1 \\ Center-guided & **37.8** & **59.5** & **39.5** & **16.9** & **40.9** & **59.5** & 69.1 G & 54.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Query selector. Our center-based query selector demonstrated superior performance with an almost negligible increase in inference time. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Query Decoder** & \(\mathbf{AP_{val}}\) & \(\mathbf{AP_{50}}\) & \(\mathbf{AP_{75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline Global sampling & 36.3 & 57.5 & 38.1 & 14.1 & 38.9 & 58.2 \\ Local sampling & **37.8** & **59.5** & **39.5** & **16.9** & **40.9** & **59.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Query decoder. Our center-guided local sampling decoder performed significantly better than the global sampling decoder in FastInst. high-resolution feature maps and precise coordinates effectively enhance the quality of query acquisition, thereby improving the model's accuracy. Sampling point number: As shown in Table 6, we evaluated the impact of different numbers of sampling points and sampling layers on query decoder performance. It can be observed that, compared to single-scale sampling, multiscale sampling produced significant improvements, especially for small and large objects. In the case of multiscale sampling, selecting 12 sampling points per layer struck a balance between high accuracy and faster inference speed. Shared-parameter pixel feature decoder: As shown in Table 7, during the training process of the pixel feature decoder, we employed both parameter-sharing and non-parameter-sharing configurations. The experimental results indicated that using a pixel feature decoder with a non-parameter-sharing configuration yielded better performance during training. Contrastive denoising training strategy: We enhanced the decoding capability of the decoder during the training of CenterInst using a denoising training strategy. As shown in Table 8, both the CenterInst and CenterInst-large models exhibit improved performance, with a significant enhancement in the decoder's decoding capability.