computer-vision-10

One-Shot Segmentation

3260 papers • 126 benchmarks • 313 datasets

( Image credit: One-Shot Learning for Semantic Segmentation )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in one-shot-segmentation-10

Trend

Dataset

Best Model

Actions

Cluttered Omniglot

Libraries

i

Use these libraries to find one-shot-segmentation-10 models and implementations

Datasets

Cluttered Omniglot

UMD-i Affrodance Dataset

Subtasks

No subtasks available.

Most implemented papers

One-Shot Learning for Semantic Segmentation

Irfan Essa, Byron Boots, Amirreza Shaban, Shray Bansal, Z. Liu•Thu Aug 31 2017

This work trains a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN), and uses this FCN to perform dense pixel-level prediction on a test image for the new semantic class.

832

Content

0

Paper Graph

Image Segmentation Using Text and Image Prompts

Alexander S. Ecker, Timo Lüddecke•Fri Dec 17 2021

This work proposes a system that can generate image segmentations based on arbitrary prompts at test time, and builds upon the CLIP model as a backbone which it extends with a transformer-based decoder that enables dense prediction.

668 0

Paper Graph

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

A. Kak, Ankit V. Manerikar•Thu Mar 09 2023

A framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN that learns to segment synthetic images using a self-supervised contrastive clustering algorithm that projects the hidden features into a compact space for per-pixel classification.

2 0

Paper Graph

One-Shot Segmentation in Clutter

M. Bethge, Claudio Michaelis, Alexander S. Ecker•Sun Mar 25 2018

An improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects is introduced, suggesting that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.

41 0

Paper Graph

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

Wanli Ouyang, Shuai Yi, Xinchi Zhou, Xianghui Yang, Bairun Wang, Kaige Chen, Luping Zhou•Thu Aug 13 2020

A framework to bridge the gap between information interaction between query and support images and to precisely localize the query objects is proposed, which outperforms other competitive methods and leads to a new state-of-the-art on both PASCAL VOC and MSCOCO dataset.

54 0

Paper Graph

Contour Transformer Network for One-Shot Segmentation of Anatomical Structures

Le Lu, Song Wang, Yuhang Lu, K. Zheng, Weijian Li, Yirui Wang, Adam P. Harrison, Chihung Lin, Jing Xiao, C. Kuo, S. Miao•Tue Dec 01 2020

This work forms anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs) and demonstrates that the one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods.

22 0

Paper Graph

Progressive One-shot Human Parsing

D. Tao, Jing Zhang, B. Thuraisingham, Haoyu He•Mon Dec 21 2020

A novel Progressive One-shot Parsing network (POPNet) to address two critical challenges, i.e., testing bias and small sizes, which is based on two collaborative metric learning modules named Attention Guidance Module and Nearest Centroid Module.

19 0

Paper Graph

Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis

Mengwei Ren, Neel Dey, M. Styner, K. Botteron, G. Gerig•Wed Jun 08 2022

This paper presents a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal images that exploits the spatiotsemporal self-similarity of learned multi- scale intra-subject image features for pretraining and develops several feature-wise regularizations that avoid degenerate representations.

24 0

Paper Graph

End-to-End One-Shot Human Parsing

D. Tao, Jing Zhang, Jianfei Cai, Bohan Zhuang•Mon May 03 2021

An end-to-end human parsing framework that can quickly adapt to the novel classes and mitigate the testing bias issue is proposed, and a contrastive loss at the prototype level is added to enforce inter-class distances.

11 0

Paper Graph

One-shot Weakly-Supervised Segmentation in Medical Images

Guotai Wang, Shaoting Zhang, Wenhui Lei, Ran Gu, Q. Su, Na Wang, Xinglong Liu, Xiaofan Zhang•Sat Nov 20 2021

An innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings is presented and shows significant improvement over the state-of-the-art methods and performs robustly even under severe class imbalance and low contrast.

2 0

Paper Graph

Adding a benchmark result helps the community track progress.