Parsing R-CNN for Instance-Level Human Analysis

Published in

Computer Vision and Pattern Recognition(2018)

External Links:

Generate Graph DownloadPDF

TL;DR

This paper presents an end-to-end pipeline for solving the instance-level human analysis, named Parsing R-CNN, which processes a set of human instances simultaneously through comprehensive considering the characteristics of region-based approach and the appearance of a human, thus allowing representing the details of instances.

Abstract

Instance-level human analysis is common in real-life scenarios and has multiple manifestations, such as human part segmentation, dense pose estimation, human-object interactions, etc. Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance. In this paper, we present an end-to-end pipeline for solving the instance-level human analysis, named Parsing R-CNN. It processes a set of human instances simultaneously through comprehensive considering the characteristics of region-based approach and the appearance of a human, thus allowing representing the details of instances. Parsing R-CNN is very flexible and efficient, which is applicable to many issues in human instance analysis. Our approach outperforms all state-of-the-art methods on CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st place in the COCO 2018 Challenge DensePose Estimation task. Code and models are publicly available.

Authors

Q. Song

2 papers

Lu Yang

1 papers

Zhihui Wang

1 papers

References50 items

Multi-Human Parsing Machines

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Instance-level Human Parsing via Part Grouping Network

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

Parsing R-CNN for Instance-Level Human Analysis

Published in

Computer Vision and Pattern Recognition(2018)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Q. Song

2 papers

Lu Yang

1 papers

Zhihui Wang

1 papers

References50 items

Multi-Human Parsing Machines

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Instance-level Human Parsing via Part Grouping Network

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

Group Normalization

Path Aggregation Network for Instance Segmentation

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

DensePose: Dense Human Pose Estimation in the Wild

Panoptic Segmentation

Detect-and-Track: Efficient Pose Estimation in Videos

FSSD: Feature Fusion Single Shot Multibox Detector

Cascade R-CNN: Delving Into High Quality Object Detection

Relation Networks for Object Detection

Learning to Segment Every Thing

Non-local Neural Networks

Rethinking Atrous Convolution for Semantic Image Segmentation

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Multiple-Human Parsing in the Wild

Detecting and Recognizing Human-Object Interactions

Deformable Convolutional Networks

Feature Pyramid Networks for Object Detection

Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

Fully Convolutional Instance-Aware Semantic Segmentation

Aggregated Residual Transformations for Deep Neural Networks

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Identity Mappings in Deep Residual Networks

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Deep Residual Learning for Image Recognition

SSD: Single Shot MultiBox Detector

Human Parsing with Contextualized Convolutional Neural Network

Rethinking the Inception Architecture for Computer Vision

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Matching-CNN meets KNN: Quasi-parametric human parsing

Deep Human Parsing with Active Template Regression

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Fully convolutional networks for semantic segmentation

Deformable part models are convolutional neural networks

Edge Boxes: Locating Object Proposals from Edges

ImageNet Large Scale Visual Recognition Challenge

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Microsoft COCO: Common Objects in Context

Visualizing and Understanding Convolutional Neural Networks

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Selective Search for Object Recognition

ImageNet classification with deep convolutional neural networks

A non-local algorithm for image denoising

Look into person: Joint human parsing and pose estimation network and a new benchmark

Field of Study

Computer Science

Journal Information

Name

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Page

5686-5696

Venue Information

Name

Computer Vision and Pattern Recognition

Type

conference

URL

https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147

Alternate Names

CVPR
Comput Vis Pattern Recognit

TL;DR

Abstract

Authors

References50 items

Multi-Human Parsing Machines

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Instance-level Human Parsing via Part Grouping Network

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

TL;DR

Abstract

Authors

References50 items

Multi-Human Parsing Machines

Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Instance-level Human Parsing via Part Grouping Network

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

Group Normalization

Path Aggregation Network for Instance Segmentation

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

DensePose: Dense Human Pose Estimation in the Wild

Panoptic Segmentation

Detect-and-Track: Efficient Pose Estimation in Videos

FSSD: Feature Fusion Single Shot Multibox Detector

Cascade R-CNN: Delving Into High Quality Object Detection

Relation Networks for Object Detection

Learning to Segment Every Thing

Non-local Neural Networks

Rethinking Atrous Convolution for Semantic Image Segmentation

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Multiple-Human Parsing in the Wild

Detecting and Recognizing Human-Object Interactions

Mask R-CNN

Deformable Convolutional Networks

Feature Pyramid Networks for Object Detection

Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields

Fully Convolutional Instance-Aware Semantic Segmentation

Aggregated Residual Transformations for Deep Neural Networks

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Identity Mappings in Deep Residual Networks

Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Deep Residual Learning for Image Recognition

SSD: Single Shot MultiBox Detector

Human Parsing with Contextualized Convolutional Neural Network

Rethinking the Inception Architecture for Computer Vision

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Fast R-CNN

Matching-CNN meets KNN: Quasi-parametric human parsing

Deep Human Parsing with Active Template Regression

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Fully convolutional networks for semantic segmentation

Deformable part models are convolutional neural networks

Edge Boxes: Locating Object Proposals from Edges

ImageNet Large Scale Visual Recognition Challenge

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Microsoft COCO: Common Objects in Context

Visualizing and Understanding Convolutional Neural Networks

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Selective Search for Object Recognition

ImageNet classification with deep convolutional neural networks

A non-local algorithm for image denoising

Look into person: Joint human parsing and pose estimation network and a new benchmark

Field of Study

Journal Information

Name

Page

Venue Information

Name

Type

URL

Alternate Names