Target Feature Augmentation and Background Optimization: Novel Approaches for Few-Shot Semantic Segmentation in Outdoor Scenes

Recently, few-shot semantic segmentation (FSS) has gained prominence due to the high costs of data annotation. While meta-learning frameworks are commonly used for feature fusion in FSS, they often yield insufficient accuracy due to scene differences. To address this problem, we propose two innovative approaches. First, we introduce a base learner with background-guided features. This additional learner recognizes targets in the base class individually and uses these predictions as background-guided features for the final target. We also propose a GramAttention module to mitigate scene differences between support and query images during background prediction. Second, we enhance the meta-learner with a separate target category selected enhancement branch. This branch utilizes known information from the support set to enhance target features, reducing background interference and improving the model’s generalization ability. We evaluated our method on the Cityscapes-3ⁱ dataset, a few-shot outdoor dataset derived from the Cityscapes database, which emphasizes semantic understanding of urban street scenes. Our experiments verified the effectiveness of the proposed approaches in improving accuracy and generalization of FSS in urban environments.