Few-shot Semantic Segmentation with Uncertainty-based Joint Prototypes

To overcome the high cost of data acquisition, few-shot semantic segmentation is studied to increase the training efficiency of limited data, but it fails to detect the narrow objects well. We find that the issue is caused by two main reasons: the enlarged receptive field of the baseline models and the high-proportional noisy labels of the narrow objects. An enlarged receptive field lets the model ignore detailed information that is important for the narrow objects, which can be affected by the same amount of noisy labels more critically than the large objects. To solve the issue, we propose a novel method to improve the performance of narrow objects in few-shot semantic segmentation. First of all, we diversify the size of the receptive field by extracting multiple prototypes from multi-level pyramidal feature maps, which is helpful to consider the detailed features of narrow objects. In addition, during model training, we simultaneously update uncertainty maps that determine the pixel-wise label reliability to detect and ignore noisy labels. We validate the proposed method, which shows impressive enhancement for narrow object segmentation both quantitatively and qualitatively over the prior research.