Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

Overview

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

figure1

Abstract

Analyzing complex scenes with DNN is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. We propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only.

We also introduce an Occlusion Challenge dataset generated from real-world segmented objects with accurate annotations and propose a taxonomy of occlusion scenarios that pose a particular challenge for computer vision.

occ_challenge_dataset


NOTICE

dataset links and model will be released in a few days. Update: 18 June

Requirments

The code uses Python 3.6 and it is tested on PyTorch GPU version 1.2, with CUDA-10.0 and cuDNN-7.5.

Installation

  1. Clone the repository with:
git clone https://github.com/XD7479/Multi-Object-Occlusion.git
cd Multi-Object-Occlusion
  1. Install requirments:
pip install -r requirements.txt

Datasets

  1. Download the KINS dataset here and the Occlusion Challenge dataset here.
  2. Enter the project folder and make links for the datasets:
ln -s  kins
ln -s  occ_challenge
  1. Download the pre-trained model here.
  2. Make links for the pre-trained model:
ln -s  models
  1. Check the configuration file configs.py for the dataset and backbone you're using:
dataset_eval = 'occ_challenge'      # kins, occ_challenge
nn_type = 'resnext'             # vgg, resnext

  1. Run the evaluation code with:
python3 eval_meanIoU.py

Segmentation Demo

demo

Citation

@misc{yuan2021robust,
      title={Robust Instance Segmentation through Reasoning about Multi-Object Occlusion}, 
      author={Xiaoding Yuan and Adam Kortylewski and Yihong Sun and Alan Yuille},
      booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
      month = jun,
      year = {2021},
      month_numeric = {6}
}

Contact

If you have any questions you can contact Xiaoding Yuan by [email protected].

You might also like...
Code and models for ICCV2021 paper
Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Robust Object Detection via Instance-Level Temporal Cycle Confusion This repo contains the implementation of the ICCV 2021 paper, Robust Object Detect

[CVPR 2021] A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts
[CVPR 2021] A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts

Visual-Reasoning-eXplanation [CVPR 2021 A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts] Project Page | Vid

[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination
[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination

InsGen - Data-Efficient Instance Generation from Instance Discrimination Data-Efficient Instance Generation from Instance Discrimination Ceyuan Yang,

This repository contains codes of ICCV2021 paper: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

SO-Pose This repository contains codes of ICCV2021 paper: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation This paper is basically an

code for CVPR paper Zero-shot Instance Segmentation

Code for CVPR2021 paper Zero-shot Instance Segmentation Code requirements python: python3.7 nvidia GPU pytorch1.1.0 GCC =5.4 NCCL 2 the other python

FreeSOLO for unsupervised instance segmentation, CVPR 2022
FreeSOLO for unsupervised instance segmentation, CVPR 2022

FreeSOLO: Learning to Segment Objects without Annotations This project hosts the code for implementing the FreeSOLO algorithm for unsupervised instanc

Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Unseen Object Clustering: Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation Introduction In this work, we propose a new method

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

Swin Transformer for Object Detection This repo contains the supported code and configuration files to reproduce object detection results of Swin Tran

Comments
  • datasets and training code

    datasets and training code

    Could you please upload the datasets and training code? Also can I use KINS dataset from this github: https://github.com/qqlu/Amodal-Instance-Segmentation-through-KINS-Dataset for your code?

    opened by sangtrx 0
Owner
Irene Yuan
Irene Yuan
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling"

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

GIST-AILAB 92 Dec 13, 2022
The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

OC-SORT Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes

Jinkun Cao 325 Jan 5, 2023
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022) Prerequisite PyTorch >= 1.2.0 P

null 16 Dec 14, 2022
git git《Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking》(CVPR 2021) GitHub:git2] 《Masksembles for Uncertainty Estimation》(CVPR 2021) GitHub:git3]

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking Ning Wang, Wengang Zhou, Jie Wang, and Houqiang Li Accepted by CVPR

NingWang 236 Dec 22, 2022
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementati

null 304 Jan 3, 2023
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

yifan liu 147 Dec 3, 2022
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation

Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation This paper has been accepted and early accessed

Yun Liu 39 Sep 20, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection, CVPR 2021. Installation A Linux pla

Tianning Yuan 269 Dec 21, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022