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].

Owner
Irene Yuan
Irene Yuan
Weakly-supervised object detection.

Wetectron Wetectron is a software system that implements state-of-the-art weakly-supervised object detection algorithms. Project CVPR'20, ECCV'20 | Pa

NVIDIA Research Projects 342 Jan 05, 2023
System Combination for Grammatical Error Correction Based on Integer Programming

System Combination for Grammatical Error Correction Based on Integer Programming This repository contains the code and scripts that implement the syst

NUS NLP Group 0 Mar 29, 2022
This repository focus on Image Captioning & Video Captioning & Seq-to-Seq Learning & NLP

Awesome-Visual-Captioning Table of Contents ACL-2021 CVPR-2021 AAAI-2021 ACMMM-2020 NeurIPS-2020 ECCV-2020 CVPR-2020 ACL-2020 AAAI-2020 ACL-2019 NeurI

Ziqi Zhang 362 Jan 03, 2023
Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation, arXiv 2021

Hypercorrelation Squeeze for Few-Shot Segmentation This is the implementation of the paper "Hypercorrelation Squeeze for Few-Shot Segmentation" by Juh

Juhong Min 165 Dec 28, 2022
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
Code for the ICME 2021 paper "Exploring Driving-Aware Salient Object Detection via Knowledge Transfer"

TSOD Code for the ICME 2021 paper "Exploring Driving-Aware Salient Object Detection via Knowledge Transfer" Usage For training, open train_test, run p

Jinming Su 2 Dec 23, 2021
Official PyTorch implementation for FastDPM, a fast sampling algorithm for diffusion probabilistic models

Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models". FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. S

Zhifeng Kong 68 Dec 26, 2022
Keras implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

Erik Linder-Norén 8.9k Jan 04, 2023
The Environment I built to study Reinforcement Learning + Pokemon Showdown

pokemon-showdown-rl-environment The Environment I built to study Reinforcement Learning + Pokemon Showdown Been a while since I ran this. Think it is

3 Jan 16, 2022
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Adversarially-Robust-Periphery Code + Data from the paper "Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks" by A

Anne Harrington 2 Feb 07, 2022
This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.

optimaladj: A library for computing optimal adjustment sets in causal graphical models This package implements the algorithms introduced in Smucler, S

Facundo Sapienza 6 Aug 04, 2022
Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

transformer-from-scratch Code for my Medium blog post: Transformers from Scratch in PyTorch Note: This Transformer code does not include masked attent

Frank Odom 27 Dec 21, 2022
Boostcamp CV Serving For Python

Boostcamp-CV-Serving Prerequisites MySQL GCP Cloud Storage GCP key file Sentry Streamlit Cloud Secrets: .streamlit/secrets.toml #DO NOT SHARE THIS I

Jungwon Seo 19 Feb 22, 2022
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
Official repository of the AAAI'2022 paper "Contrast and Generation Make BART a Good Dialogue Emotion Recognizer"

CoG-BART Contrast and Generation Make BART a Good Dialogue Emotion Recognizer Quick Start: To run the model on test sets of four datasets, Download th

39 Dec 24, 2022
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab

PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo

Atmospheric Cloud Simulation Group @ Jagiellonian University 32 Oct 18, 2022
Training deep models using anime, illustration images.

animeface deep models for anime images. Datasets anime-face-dataset Anime faces collected from Getchu.com. Based on Mckinsey666's dataset. 63.6K image

Tomoya Sawada 61 Dec 25, 2022
PyTorch CZSL framework containing GQA, the open-world setting, and the CGE and CompCos methods.

Compositional Zero-Shot Learning This is the official PyTorch code of the CVPR 2021 works Learning Graph Embeddings for Compositional Zero-shot Learni

EML Tübingen 70 Dec 27, 2022
The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

DMIRLAB 2 Mar 13, 2022
Object Tracking and Detection Using OpenCV

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, yo

Happy N. Monday 4 Aug 21, 2022