Few-Shot Semantic Segmentation
74 papers with code • 12 benchmarks • 4 datasets
Few-shot semantic segmentation (FSS) learns to segment target objects in query image given few pixel-wise annotated support image.
Benchmarks
These leaderboards are used to track progress in Few-Shot Semantic Segmentation
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Libraries
Use these libraries to find Few-Shot Semantic Segmentation models and implementations
PASCAL-5i
FSS-1000
FewSOL
GAS
Latest papers with no code
Few-Shot Fruit Segmentation via Transfer Learning
By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images.
Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Models
The ability of a robot to pick an object, known as robot grasping, is crucial for several applications, such as assembly or sorting.
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Instead, our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain.
LERENet: Eliminating Intra-class Differences for Metal Surface Defect Few-shot Semantic Segmentation
Since the relation structure of local features embedded in graph space will help to eliminate \textit{Semantic Difference}, we employ Multi-Prototype Reasoning (MPR) module, extracting local descriptors based prototypes and analyzing local-view feature relevance in support-query pairs.
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm.
Analyzing Local Representations of Self-supervised Vision Transformers
In this paper, we present a comparative analysis of various self-supervised Vision Transformers (ViTs), focusing on their local representative power.
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences.
Background Clustering Pre-training for Few-shot Segmentation
In this paper, we propose a new pre-training scheme for FSS via decoupling the novel classes from background, called Background Clustering Pre-Training (BCPT).
Language-guided Few-shot Semantic Segmentation
Few-shot learning is a promising way for reducing the label cost in new categories adaptation with the guidance of a small, well labeled support set.
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models
The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B).
