Multi-human parsing is the task of parsing multiple humans in crowded scenes.
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
SOTA for Instance Segmentation on Cityscapes test (using extra training data)
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
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
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
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
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.
#4 best model for Lane Detection on TuSimple
Without any real part segmentation training data, our method performs comparably to several supervised state-of-the-art approaches which require real part segmentation training data on Pascal-Person-Parts and COCO-DensePose datasets.