# CenterInst: Center-Based Real-Time Instance Segmentation
## 5 Conclusions  
To address the challenge of real-time instance segmentation, this paper introduced a network, CenterInst, that achieves higher accuracy and faster speed than current state-of-the-art methods. Firstly, we introduced a center-guided query selection module, which utilizes the predicted probabilities of center points to enhance the quality of the selected queries. This lightweight module significantly improves query quality without substantially increasing the inference time. Secondly, we improved the query decoding branch by proposing a center-guided query decoding batch. This branch utilizes the coordinates of center points to achieve multiscale local sampling, effectively avoiding redundant sampling features. We employed the adaptive mixing method to decode queries using sampled features, which, compared to the multihead attention mechanism in Transformers, avoids the use of global attention, reducing the introduction of irrelevant features. This makes the query decoder more efficient and accurate. Simultaneously, we simplified the pixel feature decoding branch by proposing a lightweight inference structure. During training, we used a pixel feature decoding branch with the same number of layers as the query decoder, but, during inference, only the last layer of the pixel feature decoding branch is utilized. This structure significantly reduces the model's inference time, with only a slight impact on model accuracy. Finally, we introduced the contrastive denoising training \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **CDT** & **APval** & **AP50** & **AP75** & **APS** & **APM** & **APL** \\ \hline CenterInst & - & 37.8 & 59.5 & 39.5 & **16.9** & 40.9 & 59.5 \\ CenterInst & ✓ & **38.4** & **60.0** & **40.4** & 16.3 & **42.0** & **60.8** \\ \hline CenterInst-large & - & 38.1 & 59.8 & 39.9 & 16.3 & 41.2 & **60.5** \\ CenterInst-large & ✓ & **38.6** & **60.2** & **40.8** & **17.4** & **42.2** & **60.5** \\ \hline \hline \end{tabular}  
* CDT represents the contrastive denoising training strategy. “✓” indicates the adoption of this strategy, while “-” indicates its absence. Bold data indicates that this method outperforms others in this metric.  
\end{table} 
Table 8: Contrastive denoising training strategy. We applied the contrastive denoising training strategy to both CenterInst and CenterInst-large and observed an improvement in performance for both models. Figure 2: Visualization of some predictions on the COCO dataset. We used CenterInst with a ResNet50 backbone that achieved 38.4 AP on the validation set with a speed of 54.1 FPS on a single RTX3090 Ti GPU. The first row shows the ground truth, while the second row shows the predictions. We set the confidence threshold to 0.5.  
method, which further enhanced the decoding capabilities of the decoder and improved the model accuracy. Our experimental results demonstrated that CenterInst achieves a significant improvement in performance and speed on the COCO test-dev dataset, with an AP value of 39.2 AP and an inference speed of 54.1 FPS on the ResNet50 backbone. Compared to FastInst, CenterInst achieves a 0.6 AP improvement in accuracy and a 3.8 FPS improvement in inference speed. This finding indicates that introducing center points effectively assists instance segmentation networks in sampling and query selection, thereby enhancing model accuracy and inference speed. This provides a new approach to addressing the challenge of real-time instance segmentation. Conceptualization, S.T. and L.R. ; methodology, S.T. and L.R. ; software, L.R. ; validation, S.T. ; formal analysis, S.T. and L.R. ; investigation, S.T. and L.R. ; resources, L.R. ; data curation, L.R. ; writing--original draft preparation, S.T. and L.R. ; writing--review and editing, S.T. ; visualization, L.R. ; supervision, S.T. ; project administration, S.T. ; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.