ObjProp

Introduction

This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

Installation

This repo is built using mmdetection. To install the dependencies, first clone the repository locally:

git clone https://github.com/anirudh-chakravarthy/objprop.git

Then, install PyTorch 1.1.0, torchvision 0.3.0, mmcv 0.2.12:

conda install pytorch==1.1.0 torchvision==0.3.0 -c pytorch
pip install mmcv==0.2.12

Then, install mmdetection:

python setup.py develop
# or "pip install -v -e ."

Then, install the CocoAPI for YouTube-VIS

conda install cython scipy
pip install git+https://github.com/youtubevos/cocoapi.git#"egg=pycocotools&subdirectory=PythonAPI"

Training

First, download and prepare the YouTube-VIS dataset using the following instructions.

To train ObjProp, run the following command:

python3 tools/train.py configs/masktrack_rcnn_r50_fpn_1x_youtubevos_objprop.py

In order to change the arguments such as dataset directory, learning rate, number of GPUs, etc, refer to the following configuration file configs/masktrack_rcnn_r50_fpn_1x_youtubevos_objprop.py.

Inference

Our pretrained model can be downloaded from Google Drive.

To perform inference using ObjProp, run the following command:

python3 tools/test_video.py configs/masktrack_rcnn_r50_fpn_1x_youtubevos_objprop.py [MODEL_PATH] --out [OUTPUT_PATH.json] --eval segm

A JSON file with the inference results will be saved at OUTPUT_PATH.json. To evaluate the performance, submit the result file to the evaluation server.

License

ObjProp is released under the Apache 2.0 license.

Citation

@article{Chakravarthy2021ObjProp,
  author = {Anirudh S Chakravarthy and Won-Dong Jang and Zudi Lin and Donglai Wei and Song Bai and Hanspeter Pfister},  
  title = {Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation},
  journal = {CoRR},
  volume = {abs/2111.07529},
  year = {2021},
  url = {https://arxiv.org/abs/2111.07529}
}