Liu, W., Wu, Z., Zhao, Y., Fang, Y., Foo, C.-S., Cheng, J., & Lin, G. (2023). Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation. International Journal of Computer Vision. https://doi.org/10.1007/s11263-023-01939-y
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the per- formance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized few-shot semantic segmentation (GFSSeg) has been intro- duced, aiming to predict segmentation masks for both base and novel classes. However, the current prototype-based methods do not explicitly consider the relationship between base and novel classes when updating prototypes, leading to a limited performance in identifying true categories. To address this challenge, we propose a class contrastive loss and a class relation- ship loss to regulate prototype updates and encourage a large distance between prototypes from different classes, thus distinguishing the classes from each other while maintaining the perfor- mance of the base classes. Our proposed approach achieves new state-of-the-art performance for the generalized few-shot segmentation task on PASCAL VOC and MS COCO datasets.
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A20H6b0151