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

Last update: May 03, 2022

Related tags

Deep Learning ObjProp

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}
}

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

Related tags

Overview

ObjProp

Introduction

Installation

Training

Inference

License

Citation

Owner

Anirudh S Chakravarthy

A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

Certis - Certis, A High-Quality Backtesting Engine

Deep ViT Features as Dense Visual Descriptors

Weakly Supervised End-to-End Learning (NeurIPS 2021)

Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Official implementation for (Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching, AAAI-2021)

AI-generated-characters for Learning and Wellbeing

Codes for "Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier"

Binary classification for arrythmia detection with ECG datasets.

Synthetic structured data generators

mmdetection version of TinyBenchmark.

LLVIP: A Visible-infrared Paired Dataset for Low-light Vision

This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

A curated list and survey of awesome Vision Transformers.

Tensorboard for pytorch (and chainer, mxnet, numpy, ...)

Official implementation of MSR-GCN (ICCV 2021 paper)

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

Equivariant GNN for the prediction of atomic multipoles up to quadrupoles.

Generic U-Net Tensorflow implementation for image segmentation