Few-Shot Semantic Segmentation

66 papers with code • 12 benchmarks • 3 datasets

Few-shot semantic segmentation (FSS) learns to segment target objects in query image given few pixel-wise annotated support image.


Use these libraries to find Few-Shot Semantic Segmentation models and implementations

Most implemented papers

PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

kaixin96/PANet ICCV 2019

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.

Prior Guided Feature Enrichment Network for Few-Shot Segmentation

Jia-Research-Lab/PFENet 4 Aug 2020

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 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 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 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 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 22 Dec 2021

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 13 Oct 2022

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

sourachakra/densesubgraphsaliency 20 Nov 2015

We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.