An enhanced approach for few-shot segmentation via smooth downsampling mask and label smoothing loss.

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Both methods can be used as plug-and-play modules for existing methods. Notably, our proposed method does not involve additional learnable parameters and is computationally efficient, thus achieving painless gains. To validate the effectiveness of our method, we take three publicly available models as baselines and conduct extensive experiments on three public benchmarks PASCAL-5 i , COCO-20 i and FSS-1000, and achieve considerable improvement. • Introduces a novel Smooth Downsampling Mask (SDM) method. • Proposes a label smoothing loss technique. • Demonstrates superior performance of the proposed approach on benchmark datasets. • Provides comprehensive experimental analysis and comparisons with SOTA methods. [ABSTRACT FROM AUTHOR]