Dense Gaussian Processes for Few-Shot Segmentation

Last update: Jan 07, 2023

Related tags

Deep Learning DGPNet

Overview

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation

Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxiv.org/abs/2110.03674 .

How to run

Download data

Download and unzip PASCAL and COCO images
Download and unzip PASCAL and COCO annotations (we provide link here)
Change local_config.py to point out the images and annotations. Also change slurm_launch.sh if using slurm.
Download and unzip PASCAL and COCO data splits (we provide link here)
Make sure that the data splits are at DGPNet/data_splits

Install dependencies

The dependencies are listed in DGPNet/singularity/Dockerfile21.09

Train and test model

We typically run via slurm, using

sbatch singularity/slurm_launch.sh runfiles/dgp_5shot_pascal_resnet50.py --train --test --dataset pascal --fold 0 --add_packages_to_path

Code layout

checkpoints - Checkpoints will be stored here at the end of training.
data_splits - Defines the different folds.
fss - Code is here.
local_config.py - Used to set up paths
logs - Used to store slurm checkpoints
runfiles - Any experiment we run is defined in a runfile. The runfile is launched as main to start the experiment.
singularity - We use singularity/slurm and any files related to that are stored here.
visualization - During training and testing, our code stores some visualizations. They go here.

Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

34 Oct 8, 2022

Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

103 Dec 14, 2022

PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).

PFENet This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEE

230 Dec 31, 2022

The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

9 Nov 7, 2022

CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

181 Dec 27, 2022

Few-shot Learning of GPT-3

Few-shot Learning With Language Models This is a codebase to perform few-shot "in-context" learning using language models similar to the GPT-3 paper.

224 Dec 28, 2022

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

Easy Few-Shot Learning Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you

399 Jan 8, 2023

git《FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding》(CVPR 2021) GitHub: [fig8]

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding (CVPR 2021) This repo contains the implementation of our state-of-the-art fewshot ob

233 Dec 29, 2022

Library of various Few-Shot Learning frameworks for text classification

FewShotText This repository contains code for the paper A Neural Few-Shot Text Classification Reality Check Environment setup # Create environment pyt

47 Jan 3, 2023

Comments

how to visualise the Gaussian process?

Hi there, I think your idea of using GP is very interesting. May I know how you visualize the Gaussian mean and covariance in the overview figure (Figure2 in the latest Arvix version)? Thanks in advance.

opened by ry-jojo 4
torch.linalg.cholesky warnings

Hi Joakim, When training on 10shot, I am facing with warnings like the below:

WARNING batched routines are designed for small sizes. It might be better to use the Native/Hybrid classical routines if you want good performance.

I think this warning comes from the torch.linalg.cholesky(K_ss), when K_ss's largest size() > 2048.

May I know how you deal with this warning during training? Thanks in advance!

opened by ry-jojo 2

Dense Gaussian Processes for Few-Shot Segmentation

Related tags

Overview

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation

How to run

Download data

Install dependencies

Train and test model

Code layout

You might also like...

Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).

The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

Few-shot Learning of GPT-3

Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

git《FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding》(CVPR 2021) GitHub: [fig8]

Library of various Few-Shot Learning frameworks for text classification

Comments

how to visualise the Gaussian process?

torch.linalg.cholesky warnings

Releases(eccv_video_and_poster)

eccv_video_and_poster(Sep 28, 2022)

Owner

Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

(ICCV 2021 Oral) Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation.

Official repository of PanoAVQA: Grounded Audio-Visual Question Answering in 360° Videos (ICCV 2021)

EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

KIDA: Knowledge Inheritance in Data Aggregation

Face Recognition & AI Based Smart Attendance Monitoring System.

Pure python implementation reverse-mode automatic differentiation

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Official pytorch implementation of DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces

A lightweight python AUTOmatic-arRAY library.

Implementation of the paper "Shapley Explanation Networks"

As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).

Source code for "Roto-translated Local Coordinate Framesfor Interacting Dynamical Systems"

This is the code for the paper "Motion-Focused Contrastive Learning of Video Representations" (ICCV'21).

Use evolutionary algorithms instead of gridsearch in scikit-learn

ROS-UGV-Control-Interface - Control interface which can be used in any UGV

Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

StyleSwin: Transformer-based GAN for High-resolution Image Generation