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A few-shot segmentation network robust to the background interference (RB-net) is proposed. Specifically, RB-net utilizes middle layers of feature extractors.
Abstract—Few-shot segmentation has gained significant atten- tion owning to the effectiveness in segmenting unseen classes with a few annotated images.
Jul 13, 2024 · This paper presents a novel plug-in termed ambiguity elimination network (AENet), which can be plugged into any existing cross attention-based ...
Missing: Interference. | Show results with:Interference.
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In this paper, we apply few-shot segmentation to surface defect detection based on comparing the differences between normal and abnormal products to achieve ...
We propose the integrative few-shot learning frame- work (iFSL), which learns to both classify and segment a query image using class-wise foreground maps. • We ...
Few-shot semantic segmentation (FSS) aims to generate a model for segmenting novel classes using a limited number of annotated samples.
Few-Shot Segmentation (FSS) poses significant challenges due to limited support images and large intra-class appearance discrepancies.
Nov 18, 2024 · Few-shot segmentation (FSS) aims to segment the target object in a query image using only a small set of support images and masks. ... Previous ...
We present a new weakly-supervised few-shot semantic segmentation setting and a meta-learning method for tack- ling the new challenge.
Sep 9, 2024 · Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query ...