Generalized Few-Shot Semantic Segmentation
4 papers with code • 4 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Generalized Few-Shot Semantic Segmentation
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Most implemented papers
Generalized Few-shot Semantic Segmentation
dvlab-research/gfs-seg
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CVPR 2022
Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
In addition, the terms derived from our MI-based formulation are coupled with a knowledge distillation term to retain the knowledge on base classes.
Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation
lsa1997/POP
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CVPR 2023
POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype.
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
liuweide01/HBNC
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Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.
