Abstract
Panoptic segmentation, as a key task in the field of computer vision, holds significant importance in practical applications such as autonomous driving and robot vision. Currently, among deep-learning-based panoptic segmentation methods, query-based methods have received widespread attention. However, existing methods, such as Mask2Former, typically rely on a static query mechanism. This makes it difficult for the model to adapt to changes in the number of instances in different scenes and can lead to instance loss or confusion, thus limiting performance in complex scenes. Furthermore, it is prone to insufficient feature extraction and a loss of global information. To address these problems, this paper proposes a panoptic segmentation method based on dynamic instance queries (PSM-DIQ). PSM-DIQ uses a multi-dimensional attention mechanism to enhance feature extraction, utilizes instance-activation-guided dynamic query generation to improve the ability to distinguish between different instances, and optimizes pixel-query interactions through a dual-path Transformer decoder. Experiments on the Cityscapes and MS COCO datasets show that, based on the ResNet-50 backbone, PSM-DIQ significantly outperforms the Mask2Former baseline, with PQ values improving by 1.8 and 1.7 percentage points, respectively. The experimental results verify the effectiveness of PSM-DIQ in complex scene panoptic segmentation. Finally, this work will be released as an open-source software package on GitHub (v1.0).