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
Most implemented papers
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation
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ECCV 2020
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
Part-aware Prototype Network for Few-shot Semantic Segmentation
Xiangyi1996/PPNet-PyTorch
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ECCV 2020
In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation.
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
mboudiaf/RePRI-for-Few-Shot-Segmentation
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CVPR 2021
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm.
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
Reagan1311/ASGNet
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CVPR 2021
By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation.
Few-Shot Segmentation via Cycle-Consistent Transformer
GengDavid/CyCTR
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NeurIPS 2021
Directly performing cross-attention may aggregate these features from support to query and bias the query features.
Cost Aggregation Is All You Need for Few-Shot Segmentation
Seokju-Cho/Volumetric-Aggregation-Transformer
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We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.
Feature-Proxy Transformer for Few-Shot Segmentation
jarvis73/fptrans
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With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intricate decoder to perform sophisticated pixel-wise matching, while the supervised segmentation methods use a simple linear classification head.
A dense subgraph based algorithm for compact salient image region detection
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.
