Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation.

ABSTRACT: This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks with the same architecture. Our approach leverages the varying discriminative power of these networks, resulting in rich and diverse visual features that are more informative than a pretrained classification backbone that is not optimized for dense pixel-wise classification tasks used in most state-of-the-art methods. Secondly, the pretrained semantic segmentation network serves as a base class extractor, which effectively mitigates false positives that occur during inference time and are caused by base ob

SUBMITTER: Karimi A 

PROVIDER: S-EPMC10874957 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature