computer-vision-1

Multi-Human Parsing

3260 papers • 126 benchmarks • 313 datasets

Multi-human parsing is the task of parsing multiple humans in crowded scenes. ( Image credit: Multi-Human Parsing )

(Image credit: Papersgraph)

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MHP v2.0

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Most implemented papers

Mask R-CNN

Kaiming He, Georgia Gkioxari, Ross B. Girshick, Piotr Dollár•Sun Mar 19 2017

This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

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Semantic Instance Segmentation with a Discriminative Loss Function

L. Gool, Bert De Brabandere, D. Neven•Mon Aug 07 2017

This work proposes an approach of combining an off-the-shelf network with a principled loss function inspired by a metric learning objective that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.

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Instance-Aware Semantic Segmentation via Multi-task Network Cascades

Kaiming He, Jifeng Dai•Sun Dec 13 2015

This paper presents Multitask Network Cascades for instance-aware semantic segmentation, which consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects, and develops an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.

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Multiple-Human Parsing in the Wild

Jiashi Feng, Shuicheng Yan, Yunchao Wei, Jianshu Li, Jian Zhao, Congyan Lang, Yidong Li, T. Sim•Thu May 18 2017

This work introduces a new multi-human parsing dataset and a novel multi- human parsing model named MH-Parser, which generates global parsing maps and person instance masks simultaneously in a bottom-up fashion with the help of a new Graph-GAN model.

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

Jiashi Feng, Shuicheng Yan, Jianshu Li, Jian Zhao, T. Sim, Yu Cheng, Li Zhou•Mon Apr 09 2018

A new large-scale database "Multi-Human Parsing (MHP)" is presented for algorithm development and evaluation, and NAN consistently outperforms existing state-of-the-art solutions on the MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.

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Parsing R-CNN for Instance-Level Human Analysis

Q. Song, Lu Yang, Zhihui Wang, Ming Jiang•Thu Nov 29 2018

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.

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Holistic, Instance-Level Human Parsing

Philip H. S. Torr, Anurag Arnab, Qizhu Li•Sun Sep 10 2017

This work segments the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to, and shows how this approach benefits us in obtaining segmentations at coarser granularities as well.

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UniParser: Multi-Human Parsing With Unified Correlation Representation Learning

Jiaming Chu, Lei Jin, Yinglei Teng, Jianshu Li, Yunchao Wei, Zheng Wang, Junliang Xing, Shuicheng Yan, Jian Zhao•Thu Oct 12 2023

By unifying instance-level and category-level output, UniParser circumvents manually designed post-processing techniques and surpasses state-of-the-art methods, achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP.

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Single-Stage Multi-human Parsing via Point Sets and Center-based Offsets

Junliang Xing, Jiaming Chu, Lei Jin, Jian Zhao•Fri Apr 21 2023

A high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems,i.e., locating the human body and parts.

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