Mar 10, 2023 · Abstract:Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images.

We propose a two-stage framework for weakly super- vised few-shot segmentation, consisting of initial mask generation from the image label text and iterative ...

This paper proposes a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and ...

37.7. Iterative Few-shot Semantic Segmentation from Image Label Text. 2023. 62. MLC (ResNet-101). 37.5. Mining Latent Classes for Few-shot Segmentation. 2021.

Semantic segmentation is an important task of pattern recognition [1] , which aims to allocate a category label to each pixel. With the development of deep ...

This paper proposes a new few-shot segmentation method that can effectively perceive both semantic categories and regional boundaries.

Few-shot semantic segmentation [24, 40, 45, 46, 48, 49] aims to segment novel (unseen) classes with few labelled training examples. Most state-of-the-art ...

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Sep 7, 2024 · In this paper, we advance the few-shot segmentation paradigm towards a scenario where image-level annotations are available to help the training ...

We propose a self-regularized prototype network (SRPNet) that enhances the few-shot segmentation through supervised prototype generation.