Video Instance Segmentation with a Propose-Reduce Paradigm (ICCV 2021)

Related tags

Deep LearningProposeReduce
Overview

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

TeaserImage

Installation

Please refer to INSTALL.md.

Demo

You can compute the VIS results for your own videos.

  1. Download pretrained weight.
  2. Put example videos in 'demo/inputs'. We support two types of inputs, frames directories or .mp4 files (see example for details).
  3. Run the following script and obtain the results in demo/outputs.
sh demo.sh

Data Preparation

(1) Download the videos and jsons of val set from YouTube-VIS 2019

(2) Download the videos and jsons of val set from YouTube-VIS 2021

(3) Symlink the corresponding dataset and json files to the data folder

mkdir data

data
├── valset_ytv19 --> /path/to/ytv2019/vos/valid/JPEGImages/ 
├── valid_ytv19.json --> /path/to/ytv2019/vis/valid.json
├── valset_ytv21 --> /path/to/ytv2021/vis/valid/JPEGImages/ 
├── valid_ytv21.json --> /path/to/ytv2021/vis/valid/instances.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
Dataset Method Backbone CA Reduce AP [email protected] download
YouTube-VIS 2019 Seq Mask R-CNN ResNet-50 40.8 49.9 model | scripts
YouTube-VIS 2019 Seq Mask R-CNN ResNet-50 ✓ 42.5 56.8 scripts
YouTube-VIS 2019 Seq Mask R-CNN ResNet-101 43.8 52.7 model | scripts
YouTube-VIS 2019 Seq Mask R-CNN ResNet-101 ✓ 45.2 59.0 scripts
YouTube-VIS 2019 Seq Mask R-CNN ResNeXt-101 47.6 56.7 model | scripts
YouTube-VIS 2019 Seq Mask R-CNN ResNeXt-101 ✓ 48.8 62.2 scripts
YouTube-VIS 2021 Seq Mask R-CNN ResNet-50 39.6 47.5 model | scripts
YouTube-VIS 2021 Seq Mask R-CNN ResNet-50 ✓ 41.7 54.9 scripts
YouTube-VIS 2021 Seq Mask R-CNN ResNeXt-101 45.6 52.9 model | scripts
YouTube-VIS 2021 Seq Mask R-CNN ResNeXt-101 ✓ 47.2 57.6 scripts

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.

TODOs

Citation

If you find this work useful in your research, please cite:

@article{lin2021video,
  title={Video Instance Segmentation with a Propose-Reduce Paradigm},
  author={Lin, Huaijia and Wu, Ruizheng and Liu, Shu and Lu, Jiangbo and Jia, Jiaya},
  booktitle={IEEE International Conference on Computer Vision (ICCV)},
  year={2021}
}

Contact

If you have any questions regarding the repo, please feel free to contact me ([email protected]) or create an issue.

Acknowledgments

This repo is based on MMDetection, MaskTrackRCNN, STM, MMCV and COCOAPI.

Owner
DV Lab
Deep Vision Lab
DV Lab
GitHub Repository
A Real-World Benchmark for Reinforcement Learning based Recommender System

RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System RL4RS is a real-world deep reinforcement learning recommender system

121 Dec 01, 2022
Pytorch implementation of FlowNet by Dosovitskiy et al.

FlowNetPytorch Pytorch implementation of FlowNet by Dosovitskiy et al. This repository is a torch implementation of FlowNet, by Alexey Dosovitskiy et

Clément Pinard 762 Jan 02, 2023
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations This repo contains official code for the NeurIPS 2021 paper Imi

Jiayao Zhang 2 Oct 18, 2021
This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML)

package tests docs license stats support This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML

National Center for Cognitive Research of ITMO University 482 Dec 26, 2022
Predicting Tweet Sentiment Maching Learning and streamlit

Predicting-Tweet-Sentiment-Maching-Learning-and-streamlit (I prefere using Visual Studio Code ) Open the folder in VS Code Run the first cell in requi

1 Nov 20, 2021
Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Semi-supevised Semantic Segmentation with High- and Low-level Consistency This Pytorch repository contains the code for our work Semi-supervised Seman

123 Dec 30, 2022
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen

Ryan Wesslen 1 Feb 08, 2022
Code for the paper titled "Prabhupadavani: A Code-mixed Speech Translation Data for 25 languages"

Prabhupadavani: A Code-mixed Speech Translation Data for 25 languages Code for the paper titled "Prabhupadavani: A Code-mixed Speech Translation Data

Ayush Daksh 12 Dec 01, 2022
Pytorch based library to rank predicted bounding boxes using text/image user's prompts.

pytorch_clip_bbox: Implementation of the CLIP guided bbox ranking for Object Detection. Pytorch based library to rank predicted bounding boxes using t

Sergei Belousov 50 Nov 27, 2022
Official PyTorch implementation of "Adversarial Reciprocal Points Learning for Open Set Recognition"

Adversarial Reciprocal Points Learning for Open Set Recognition Official PyTorch implementation of "Adversarial Reciprocal Points Learning for Open Se

Guangyao Chen 78 Dec 28, 2022
Stroke-predictions-ml-model - Machine learning model to predict individuals chances of having a stroke

stroke-predictions-ml-model machine learning model to predict individuals chance

Alex Volchek 1 Jan 03, 2022
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

tf-metal-experiments TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) Setup This is tested on M1 series Apple Silicon SOC only. Te

Timothy Liu 161 Jan 03, 2023
Use .csv files to record, play and evaluate motion capture data.

Purpose These scripts allow you to record mocap data to, and play from .csv files. This approach facilitates parsing of body movement data in statisti

21 Dec 12, 2022
Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Distant Supervision for Scene Graph Generation Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation. Introduction The pape

THUNLP 23 Dec 31, 2022
Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

About This repository the official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning. The config files contain the s

Dynamic Vision and Learning Group 41 Dec 10, 2022
Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Supporting Clustering with Contrastive Learning SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ram

231 Jan 05, 2023
Quantum-enhanced transformer neural network

Example of a Quantum-enhanced transformer neural network Get the code: git clone https://github.com/rdisipio/qtransformer.git cd qtransformer Create

Riccardo Di Sipio 61 Nov 08, 2022
This repository builds a basic vision transformer from scratch so that one beginner can understand the theory of vision transformer.

vision-transformer-from-scratch This repository includes several kinds of vision transformers from scratch so that one beginner can understand the the

1 Dec 24, 2021
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

Andy Zou 79 Dec 30, 2022