Multi-human parsing is the task of parsing multiple humans in crowded scenes.
( Image credit: Multi-Human Parsing )
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
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
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
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
SOTA for Human Part Segmentation on CIHP
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.
SOTA for Human Part Segmentation on PASCAL-Person-Part (using extra training data)
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 Multi-Human Parsing on MHP v1.0