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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
In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data.
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Ranked #4 on Human Part Segmentation on PASCAL-Person-Part (using extra training data)
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #2 on Human Part Segmentation on CIHP
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
Ranked #1 on Semantic Segmentation on LIP val
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 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.
Ranked #1 on Human Part Segmentation on PASCAL-Person-Part (using extra training data)
In this technical report, we present two novel datasets for image scene understanding.