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

Related tags

Deep LearningObjProp
Overview

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 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

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}
}
Owner
Anirudh S Chakravarthy
MS in Computer Vision, CMU | Research Intern, Harvard VCG | B.E. Computer Science, BITS Pilani. Visit my site for more.
Anirudh S Chakravarthy
GNEE - GAT Neural Event Embeddings

GNEE - GAT Neural Event Embeddings This repository contains source code for the GNEE (GAT Neural Event Embeddings) method introduced in the paper: "Se

João Pedro Rodrigues Mattos 0 Sep 15, 2021
Pytorch implementation of VAEs for heterogeneous likelihoods.

Heterogeneous VAEs Beware: This repository is under construction 🛠️ Pytorch implementation of different VAE models to model heterogeneous data. Here,

Adrián Javaloy 35 Nov 29, 2022
OntoProtein: Protein Pretraining With Ontology Embedding

OntoProtein This is the implement of the paper "OntoProtein: Protein Pretraining With Ontology Embedding". OntoProtein is an effective method that mak

ZJUNLP 80 Dec 14, 2022
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

Quasi-Recurrent Neural Network (QRNN) for PyTorch Updated to support multi-GPU environments via DataParallel - see the the multigpu_dataparallel.py ex

Salesforce 1.3k Dec 28, 2022
A deep learning framework for historical document image analysis

DIVA-DAF Description A deep learning framework for historical document image analysis. How to run Install dependencies # clone project git clone https

9 Aug 04, 2022
Align and Prompt: Video-and-Language Pre-training with Entity Prompts

ALPRO Align and Prompt: Video-and-Language Pre-training with Entity Prompts [Paper] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H

Salesforce 127 Dec 21, 2022
Landmarks Recogntion Web application using Streamlit.

Landmark Recognition Web-App using Streamlit Watch Tutorial for this project Source Trained model landmarks_classifier_asia_V1/1 is taken from the Ten

Kushal Bhavsar 5 Dec 12, 2022
Dyalog-apl-docset - Dyalog APL Dash Docset Generator

Dyalog APL Dash Docset Generator o alasa e kili sona kepeken tenpo lili a A Dash

Maciej Goszczycki 1 Jan 10, 2022
JAX-based neural network library

Haiku: Sonnet for JAX Overview | Why Haiku? | Quickstart | Installation | Examples | User manual | Documentation | Citing Haiku What is Haiku? Haiku i

DeepMind 2.3k Jan 04, 2023
An Open Source Machine Learning Framework for Everyone

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

170.1k Jan 05, 2023
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Fudan Zhang Vision Group 897 Jan 05, 2023
Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences"

Syntax-Customized-Video-Captioning Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences". This is my second w

3 Dec 05, 2022
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

26 Nov 29, 2022
A library that allows for inference on probabilistic models

Bean Machine Overview Bean Machine is a probabilistic programming language for inference over statistical models written in the Python language using

Meta Research 234 Dec 29, 2022
Demonstration of transfer of knowledge and generalization with distillation

Distilling-the-Knowledge-in-a-Neural-Network This is an implementation of a part of the paper "Distilling the Knowledge in a Neural Network" (https://

26 Nov 25, 2022
The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

1 Oct 14, 2021
Controlling Hill Climb Racing with Hand Tacking

Controlling Hill Climb Racing with Hand Tacking Opened Palm for Gas Closed Palm for Brake

Rohit Ingole 3 Jan 18, 2022
This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Models used for prediction Diabetes and further the basic theory and working of Gold nanoparticles.

GoldNanoparticles This GitHub repo consists of Code and Some results of project- Diabetes Treatment using Gold nanoparticles. These Consist of ML Mode

1 Jan 30, 2022