Multi-Human Parsing Edit Task
Computer Vision • Human Parsing
About
Multi-human parsing is the task of parsing multiple humans in crowded scenes.
( Image credit: Multi-Human Parsing )
Benchmarks
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Datasets
Greatest papers with code
Mask R-CNN
ICCV 2017
• tensorflow/models
• 
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on
Real-Time Object Detection
on COCO minival
(MAP metric)
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
• ZhaoJ9014/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.
Ranked #1 on
Multi-Human Parsing
on MHP v2.0
AUTONOMOUS DRIVING INSTANCE SEGMENTATION MULTI-HUMAN PARSING PERSON RE-IDENTIFICATION SALIENCY PREDICTION SEMANTIC SEGMENTATION
Multiple-Human Parsing in the Wild
• ZhaoJ9014/Multi-Human-Parsing
•
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.
Ranked #2 on
Multi-Human Parsing
on MHP v2.0
Instance-aware Semantic Segmentation via Multi-task Network Cascades
We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure.
Ranked #3 on
Multi-Human Parsing
on PASCAL-Part
Semantic Instance Segmentation with a Discriminative Loss Function
• Wizaron/instance-segmentation-pytorch
• 
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.
Ranked #4 on
Multi-Human Parsing
on MHP v1.0
INSTANCE SEGMENTATION LANE DETECTION METRIC LEARNING MULTI-HUMAN PARSING SEMANTIC SEGMENTATION
Parsing R-CNN for Instance-Level Human Analysis
CVPR 2019
• soeaver/Parsing-R-CNN
•
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
Ranked #1 on
Human Part Segmentation
on MHP v2.0
HUMAN-OBJECT INTERACTION DETECTION HUMAN PART SEGMENTATION MULTI-HUMAN PARSING POSE ESTIMATION
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
• kevinlin311tw/CDCL-human-part-segmentation
•
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.
Ranked #1 on
Human Part Segmentation
on PASCAL-Part
(using extra training data)
DOMAIN ADAPTATION HUMAN PART SEGMENTATION MULTI-HUMAN PARSING POSE ESTIMATION
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.
Ranked #2 on
Multi-Human Parsing
on PASCAL-Part