ProposeReduce
Video Instance Segmentation with a Propose-Reduce Paradigm (ICCV 2021)
Install / Use
/learn @JIA-Lab-research/ProposeReduceREADME
Propose-Reduce VIS
This repo contains the official implementation for the paper:
Video Instance Segmentation with a Propose-Reduce Paradigm
Huaijia Lin*, Ruizheng Wu*, Shu Liu, Jiangbo Lu, Jiaya Jia
ICCV 2021 | Paper
Installation
Please refer to INSTALL.md.
Demo
You can compute the VIS results for your own videos.
- Download a pretrained ResNet-101 and put it in pretrained folder.
mkdir pretrained
- Put example videos in 'demo/inputs'. We support two types of inputs, frames directories or .mp4 files (see example for details).
- Run the following script and obtain the results in demo/outputs.
sh demo.sh
Data Preparation
(1) Download the videos and jsons of train and val sets from YouTube-VIS 2019
(2) Download the videos and jsons of train and val sets from YouTube-VIS 2021
(3) Download the trainval set of DAVIS-UVOS
(4) Download other pre-computed jsons from data
(5) Symlink the corresponding dataset and json files to the data folder
mkdir data
data
├── trainset_ytv19 --> /path/to/ytv2019/vos/train/JPEGImages/
├── train_ytv19.json --> /path/to/ytv2019/vis/train.json
├── valset_ytv19 --> /path/to/ytv2019/vos/valid/JPEGImages/
├── valid_ytv19.json --> /path/to/ytv2019/vis/valid.json
├── trainset_ytv21 --> /path/to/ytv2021/vis/train/JPEGImages/
├── train_ytv21.json --> /path/to/ytv2021/vis/train/instances.json
├── valset_ytv21 --> /path/to/ytv2021/vis/valid/JPEGImages/
├── valid_ytv21.json --> /path/to/ytv2021/vis/valid/instances.json
├── trainvalset_davis --> /path/to/DAVIS-UnVOS/DAVIS-trainval/JPEGImages/480p/
├── train_davis.json --> /path/to/pre-computed/train_davis.json
├── valid_davis.json --> /path/to/pre-computed/valid_davis.json
Results
We provide the results of several pretrained models and corresponding scripts on different backbones. The results have slight differences from the paper because we make minor modifications to the inference codes.
Download the pretrained models and put them in pretrained folder.
mkdir pretrained
Evaluation
YouTube-VIS 2019: A json file will be saved in ../Results_ytv19 folder. Please zip and upload to the codalab server.
YouTube-VIS 2021: A json file will be saved in ../Results_ytv21 folder. Please zip and upload to the codalab server.
DAVIS-UVOS: Color masks will be saved in ../Results_davis folder. Please use the official code for evaluation.
Training
To reproduce t
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