Multi-Human Parsing
Multi-human parsing is the task of parsing multiple humans in crowded scenes.
State-of-the-art leaderboards
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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.
#2 best model for
Multi-Human Parsing
on MHP v1.0
HUMAN PART SEGMENTATION INSTANCE SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING OBJECT DETECTION SEMANTIC SEGMENTATION
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.
#3 best model for
Multi-Human Parsing
on PASCAL-Person-Part
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
• ZhaoJ9014/Multi-Human-Parsing_MHP
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.
SOTA for 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_MHP
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.
#2 best model for
Multi-Human Parsing
on MHP v2.0
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.
SOTA for Pose Estimation on DensePose-COCO
HUMAN-OBJECT INTERACTION DETECTION HUMAN PART SEGMENTATION MULTI-HUMAN PARSING POSE ESTIMATION
Semantic Instance Segmentation with a Discriminative Loss Function
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.
#3 best model for
Lane Detection
on TuSimple
INSTANCE SEGMENTATION LANE DETECTION METRIC LEARNING MULTI-HUMAN PARSING SEMANTIC SEGMENTATION