Multi-Human Parsing

10 papers with code • 3 benchmarks • 3 datasets

Multi-human parsing is the task of parsing multiple humans in crowded scenes.

( Image credit: Multi-Human Parsing )

Most implemented papers

Mask R-CNN

tensorflow/models ICCV 2017

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

Semantic Instance Segmentation with a Discriminative Loss Function

Wizaron/instance-segmentation-pytorch 8 Aug 2017

In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, 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.

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

kevinlin311tw/CDCL-human-part-segmentation 11 Jul 2019

On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.

Instance-aware Semantic Segmentation via Multi-task Network Cascades

daijifeng001/MNC CVPR 2016

We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.

Multiple-Human Parsing in the Wild

ZhaoJ9014/Multi-Human-Parsing_MHP 19 May 2017

To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.

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

open-mmlab/mmpose 10 Apr 2018

Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.

Parsing R-CNN for Instance-Level Human Analysis

soeaver/Parsing-R-CNN CVPR 2019

Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.

Holistic, Instance-Level Human Parsing

torrvision/caffe-tvg 11 Sep 2017

We address this problem by segmenting 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.

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

cjm-sfw/SMP 22 Apr 2023

We instead present 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.

UniParser: Multi-Human Parsing with Unified Correlation Representation Learning

cjm-sfw/Uniparser 13 Oct 2023

Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information.