1) Using the center's representation to conduct panoptic segmentation is too similar to PanopticFCN. The core difference would be the island centers for stuff, however, according to Table 6, it does not make significant improvements.
2) Although MaskConver gets significantly better performance than previous works, it is not clear where these improvements come from. It lacks a roadmap-like ablation study from the baseline to MaskConver. For example, in Table 5, the backbones and input sizes are all different among different models, which is not a fair or clear comparison.
3) The novelty of this paper is limited, as it does not propose new modules or training strategies. As it does not provide detailed ablations, it would be susceptible that the improvements mainly come from a highly engineered strong baseline.
4) Some other representative panoptic segmentation models are not compared, like PanopticFPN, Mask2Former, etc.
4) Some other representative panoptic segmentation models are not compared, like PanopticFPN, Mask2Former, etc.