Segment object with few examples using multi-level prototype generation with Bao
Segment object with few examples using multi-level prototype generation with Bao
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2312.06474
arXiv PDF paper https://arxiv.org/pdf/2312.06474.pdf
For few-shot semantic segmentation, the primary task is to extract class-specific intrinsic information from limited labeled data.
... semantic ambiguity and inter-class similarity of previous methods limit the accuracy of pixel-level foreground-background classification ... propose the Relevant Intrinsic Feature Enhancement Network (RiFeNet).
To improve the semantic consistency of foreground instances, ... propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.
Notably, during testing, the proposed unlabeled branch is excluded without extra unlabeled data and computation.
... extend the inter-class variability between foreground and background by proposing a novel multi-level prototype generation and interaction module.
... RiFeNet surpasses the state-of-the-art methods on PASCAL-5i and COCO benchmarks.
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contactÂ
Web site with my other posts by category https://morrislee1234.wixsite.com/websiteÂ
