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Segment humans in image after self-supervised training on multiple views

Segment humans in image after self-supervised training on multiple views


Self-supervised Human Detection and Segmentation via Multi-view Consensus

arXiv paper abstract https://arxiv.org/abs/2012.05119



Self-supervised detection and segmentation of foreground objects in complex scenes is gaining attention as their fully-supervised counterparts require overly large amounts of annotated data to deliver sufficient accuracy in domain-specific applications.


However, existing self-supervised approaches predominantly rely on restrictive assumptions on appearance and motion, which precludes their use in scenes depicting highly dynamic activities or involve camera motion.


... propose using a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training


... learn a joint distribution of proposals over multiple views.


At inference time, our method operates on single RGB images.


... approach outperforms state-of-the-art self-supervised person detection and segmentation techniques on images that visually depart from those of standard benchmarks, as well as on those of the classical Human3.6M dataset.



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