CANDY: Category-Kernelized Dynamic Convolution for Instance Segmentation
Yao Lu, Zhiyi Chen, Zehui Chen, Jie Hu, Liujuan Cao, Shengchuan Zhang · 2023
Instance segmentation has been dominated by the paradigm that predicts masks using local RoI features and simplicity frameworks based on global mask prediction. Despite the comparable performance between local-based and global-based approaches, the AP results of objects on different scales vary significantly. In this paper, we first point out that the key factor to bridging such a gap lies in the utilization of local RoI information for global mask prediction. Then, we observe a ’class-agnostic segmentation’ problem exists in the nearby region of interesting objects after implementing the above combination. To overcome this issue, we further propose a CAtegory-kerNelized DYnamic (CANDY) convolution. Benefiting from it, the discriminative ability of the resulting instance segmentation framework, i.e., CANDY-Mask, on foreground objects is significantly enhanced. Extensive experiments on the MS-COCO dataset are conducted to verify the performance of CANDY-Mask. Our proposed CANDY-Mask obtains 48.1% boxes AP and 40.7% masks AP on the MS-COCO test-dev set with ResNet50 backbone, achieving state-of-the-art among various models.