One-Shot Segmentation

15 papers with code • 1 benchmarks • 3 datasets

Most implemented papers

One-Shot Learning for Semantic Segmentation

lzzcd001/OSLSM 11 Sep 2017

Low-shot learning methods for image classification support learning from sparse data.

Image Segmentation Using Text and Image Prompts

timojl/clipseg CVPR 2022

After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.

Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

avm-debatr/ganecdotes 10 Mar 2023

Our framework is based on the observation that the multi-scale hidden features in the GAN generator hold useful semantic information that can be utilized for automatic on-the-fly segmentation of the generated images.

One-Shot Segmentation in Clutter

michaelisc/cluttered-omniglot ICML 2018

We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example.

BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation

Wi-sc/BriNet 14 Aug 2020

(2) The object categories at the training and inference stages have no overlap, leaving the inter-class gap.

Contour Transformer Network for One-shot Segmentation of Anatomical Structures

rudylyh/CTN_data 2 Dec 2020

In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.

Progressive One-shot Human Parsing

Charleshhy/One-shot-Human-Parsing 22 Dec 2020

In this paper, we devise a novel Progressive One-shot Parsing network (POPNet) to address two critical challenges , i. e., testing bias and small sizes.

End-to-end One-shot Human Parsing

Charleshhy/One-shot-Human-Parsing 4 May 2021

To address three main challenges in OSHP, i. e., small sizes, testing bias, and similar parts, we devise an End-to-end One-shot human Parsing Network (EOP-Net).

One-shot Weakly-Supervised Segmentation in Medical Images

lwhyc/oneshot_weaklyseg 21 Nov 2021

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.

Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis

mengweiren/longitudinal-representation-learning 9 Jun 2022

Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation.