Generic Foreground Segmentation in Images

Overview

Pixel Objectness

The following repository contains pretrained model for pixel objectness.

Please visit our project page for the paper and visual results.

If you use this in your research, please cite the following paper:

@article{pixelobjectness,
  Author = {Jain, Suyog and Xiong, Bo and Grauman, Kristen},
  Journal = {arXiv preprint arXiv:1701.05349},
  Title = {Pixel Objectness},
  Year = {2017}
}

These models are freely available for research and academic purposes. However it's patent pending, so please contact us for any commercial use.

Using the pretrained models:

This model is trained using Deeplab-v1 caffe library. Please cite [1] and [2] if you use the code.

  • Setup: Download and install Deeplab-v1 from here

  • Refer to demo.py for step-by-step instruction on how to run the code.

  • Store the images that you want to process in the images folder.

  • Update the caffe binary path and image extension variable in demo.py

  • Running demo.py will produce three files 1) image_list.txt : contains list of of input images, 2) output_list.txt: contains names to be used to store the output of pixel objectness 3) test.protoxt: prototxt file required for loading the pretrained model.

  • Please resize your images so that the maximum side is < 513, otherwise update the crop_size value in test_template.prototxt. Bigger crop sizes require larger gpu memory.

Visualizing the results:

After execution demo.py will store pixel objectness results as matlab files.

Please refer to show_results.m to see how to visualize and extract foreground masks.

Please cite these too if you use the code:

[1] Caffe:

@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}

[2] Deeplab-v1:

@inproceedings{chen14semantic,
title={Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs},
author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
booktitle={ICLR},
url={http://arxiv.org/abs/1412.7062},
year={2015}
}
Owner
Suyog Jain
PhD in Computer Vision, Research Scientist at PathAI
Suyog Jain
Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

AstraZeneca 79 Jan 05, 2023
On Out-of-distribution Detection with Energy-based Models

On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr

Sven 19 Aug 07, 2022
Tensorflow python implementation of "Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos"

Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos This repository is the official tensorflow python implementation

Yasamin Jafarian 287 Jan 06, 2023
DeceFL: A Principled Decentralized Federated Learning Framework

DeceFL: A Principled Decentralized Federated Learning Framework This repository comprises codes that reproduce experiments in Ye, et al (2021), which

Huazhong Artificial Intelligence Lab (HAIL) 10 May 31, 2022
Neural Contours: Learning to Draw Lines from 3D Shapes (CVPR2020)

Neural Contours: Learning to Draw Lines from 3D Shapes This repository contains the PyTorch implementation for CVPR 2020 Paper "Neural Contours: Learn

93 Dec 16, 2022
NOMAD - A blackbox optimization software

################################################################################### #

Blackbox Optimization 78 Dec 29, 2022
Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC.

Repositorio de los Laboratorios de Análisis Numérico / Análisis Numérico I de FAMAF, UNC. Para los Laboratorios de la materia, vamos a utilizar el len

Luis Biedma 18 Dec 12, 2022
Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Sh

Syed Waqas Zamir 859 Dec 22, 2022
Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

Do Not Trust Prediction Scores for Membership Inference Attacks Abstract: Membership inference attacks (MIAs) aim to determine whether a specific samp

<a href=[email protected]"> 3 Oct 25, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
🥈78th place in Riiid Answer Correctness Prediction competition

Riiid Answer Correctness Prediction Introduction This repository is the code that placed 78th in Riiid Answer Correctness Prediction competition. Requ

Jungwoo Park 10 Jul 14, 2022
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

Tianhong Dai 6 Jul 18, 2022
Gluon CV Toolkit

Gluon CV Toolkit | Installation | Documentation | Tutorials | GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in

Distributed (Deep) Machine Learning Community 5.4k Jan 06, 2023
This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
Github Traffic Insights as Prometheus metrics.

github-traffic Github Traffic collects your repository's traffic data and exposes it as Prometheus metrics. Grafana dashboard that displays the metric

Grafana Labs 34 Oct 27, 2022
g2o: A General Framework for Graph Optimization

g2o - General Graph Optimization Linux: Windows: g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has bee

Rainer Kümmerle 2.5k Dec 30, 2022
A unified framework to jointly model images, text, and human attention traces.

connect-caption-and-trace This repository contains the reference code for our paper Connecting What to Say With Where to Look by Modeling Human Attent

Meta Research 73 Oct 24, 2022
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t

Marco Cerliani 422 Jan 08, 2023
Accurate identification of bacteriophages from metagenomic data using Transformer

PhaMer is a python library for identifying bacteriophages from metagenomic data. PhaMer is based on a Transorfer model and rely on protein-based vocab

Kenneth Shang 9 Nov 30, 2022