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 )
PASCAL-Part
MHP
CCIHP
Most implemented papers
Mask R-CNN
tensorflow/models
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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
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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
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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
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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
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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
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
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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
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
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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
Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information.
