[CVPR 2021] MiVOS - Scribble to Mask module

Last update: Dec 22, 2022

Overview

MiVOS (CVPR 2021) - Scribble To Mask

Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang

A simplistic network that turns scribbles to mask. It supports multi-object segmentation using soft-aggregation. Don't expect SOTA results from this model!

Overall structure and capabilities

	MiVOS	Mask-Propagation	Scribble-to-Mask
DAVIS/YouTube semi-supervised evaluation	❌	✔️	❌
DAVIS interactive evaluation	✔️	❌	❌
User interaction GUI tool	✔️	❌	❌
Dense Correspondences	❌	✔️	❌
Train propagation module	❌	✔️	❌
Train S2M (interaction) module	❌	❌	✔️
Train fusion module	✔️	❌	❌
Generate more synthetic data	✔️	❌	❌

Requirements

The package versions shown here are the ones that I used. You might not need the exact versions.

PyTorch 1.6.0
torchvision 0.7.0
opencv-contrib 4.2.0
davis-interactive (https://github.com/albertomontesg/davis-interactive)
gitpython for training
gdown for downloading pretrained models

Refer to the official PyTorch guide for installing PyTorch/torchvision. The rest can be installed by:

pip install opencv-contrib-python gitpython gdown

Pretrained model

Download and put the model in ./saves/. Alternatively use the provided download_model.py.

[OneDrive Mirror]

Interactive GUI

python interactive.py --image <image>

Controls:

Mouse Left - Draw scribbles
Mouse middle key - Switch positive/negative
Key f - Commit changes, clear scribbles
Key r - Clear everything
Key d - Switch between overlay/mask view
Key s - Save masks into a temporary output folder (./output/)

Known issues

The model almost always needs to focus on at least one object. It is very difficult to erase all existing masks from an image using scribbles.

Training

Datasets

Download and extract LVIS training set.
Download and extract a set of static image segmentation datasets. These are already downloaded for you if you used the download_datasets.py in Mask-Propagation.

├── lvis
│   ├── lvis_v1_train.json
│   └── train2017
├── Scribble-to-Mask
└── static
    ├── BIG_small
    └── ...

Commands

Use the deeplabv3plus_resnet50 pretrained model provided here.

CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=4 python -m torch.distributed.launch --master_port 9842 --nproc_per_node=2 train.py --id s2m --load_deeplab <path_to_deeplab.pth>

Credit

Deeplab implementation and pretrained model: https://github.com/VainF/DeepLabV3Plus-Pytorch.

Citation

Please cite our paper if you find this repo useful!

@inproceedings{MiVOS_2021,
  title={Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion},
  author={Cheng, Ho Kei and Tai, Yu-Wing and Tang, Chi-Keung},
  booktitle={CVPR},
  year={2021}
}

Contact: [email protected]

Comments

AttributeError: Caught AttributeError in DataLoader worker process 0

Hello! I followed the instructions of the training command, it has thrown an error about AttributeError. I put the static folder outside this repository as you mentioned. It is confusing that I can use the same datasets for the pretraining propagation module, the train.py in Mask-Propagation works fine.

opened by xwhkkk 2
git.exc.InvalidGitRepositoryError when running train.py

Hello! I followed the instruction of the training command, but it has thrown an error about GitRepositoryError. I used command : CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=4 python -m torch.distributed.launch --master_port 1842 --nproc_per_node=2 train.py --id s2m --load_deeplab ./deeplab_resnet50/best_deeplabv3plus_resnet50_voc_os16.pth, and I have 2 GPUs. Could you give me some suggestions?

opened by xwhkkk 2
About evaluation of the model

Hi,

thank you for the nice work.

I have a concern about the evaluation of the model. Because there is no validation set to pick the best model. It may has a potential overfitting problem. (Or what should the validation set for interactive segmentation look like? If there is a unified standard, it will be more helpful for everyone to compare their methods.)

In interactive object segmentation setting, is this setting popular? I am new here for the interactive segmentation. Wish to solve my concern, thank you.

opened by Limingxing00 2
Question about Local Control Strategy

A simple but practical segmentation tool! I've read your paper, and it says that local control strategy is used in S2M. However, I don't find the local control step in this code. Why don't you provide it in this tool? Will local control make significant difference to the performance?

opened by distillation-dcf 1
DeepLabv3 pre-trained models

Hello,

I wanted to mention that in order to train S2M from scratch, using the deeplabv3_resnet50 pre-trained model provided in this repo, returns the following error: KeyError: 'classifier.classifier.0.convs.0.0.weight. Meaning that the weights from this layer are not present in deeplabv3_resnet50. But using the deeplabv3plus_resnet50 from the same repo executes without errors.

Best!

opened by UndecidedBoy 1
saving error

Hello! Thanks for sharing your code. When I run python interactive.py and want to save the masks, appeared following error.

Could you give me some suggestions?

opened by xwhkkk 3
Fix simple issues and allow for cpu only use

I had to make some changes to be able to use the code on cpu only system and had troubles saving the mask from the interactive GUI and fixed it. Thanks for the great work.

opened by rami-alloush 3

Releases(1.0)

1.0(Mar 14, 2021)

Pretrained model
Source code(tar.gz)
Source code(zip)
s2m.pth(152.04 MB)

Owner

Rex Cheng

@ HKUST.

GitHub Repository https://hkchengrex.github.io/MiVOS/

Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

39 Sep 28, 2022

Fit Fast, Explain Fast

FastExplain Fit Fast, Explain Fast Installing pip install fast-explain About FastExplain FastExplain provides an out-of-the-box tool for analysts to

8 Dec 15, 2022

End-To-End Memory Network using Tensorflow

MemN2N Implementation of End-To-End Memory Networks with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset. Get Started git clo

339 Oct 27, 2022

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

249 Dec 22, 2022

Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel fastshap was designed to be: Fast Calculating shap values can take an extremely long time. fastshap utilizes

22 Sep 24, 2022

Advantage Actor Critic (A2C): jax + flax implementation

Advantage Actor Critic (A2C): jax + flax implementation Current version supports only environments with continious action spaces and was tested on muj

3 Jan 23, 2022

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations

Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations Code repo for paper Trans-Encoder: Unsupervised sentence-pa

101 Dec 29, 2022

TensorLight - A high-level framework for TensorFlow

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced feature

10 Jul 31, 2022

A Free and Open Source Python Library for Multiobjective Optimization

Platypus What is Platypus? Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs)

424 Dec 18, 2022

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

59 Dec 17, 2022

Robotics environments

Robotics environments Details and documentation on these robotics environments are available in OpenAI's blog post and the accompanying technical repo

121 Dec 28, 2022

Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation by Lukas Hoyer, Dengxin Dai, and Luc Van Gool [Arxiv] [Paper] Overview Unsup

149 Dec 28, 2022

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting [Paper] [Project Website] [Google Colab] We propose a method for converting a

6.2k Jan 01, 2023

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

PixMix Introduction In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard te

79 Dec 30, 2022

Analysis of rationale selection in neural rationale models

Neural Rationale Interpretability Analysis We analyze the neural rationale models proposed by Lei et al. (2016) and Bastings et al. (2019), as impleme

3 Aug 31, 2022

[NeurIPS 2020] Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition"

Balanced Meta-Softmax Code for the paper Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu

65 Dec 21, 2022

Implementation of ECCV20 paper: the devil is in classification: a simple framework for long-tail object detection and instance segmentation

Implementation of our ECCV 2020 paper The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation This repo contains code o

98 Sep 17, 2022