Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Detection and Segmentation via Multi-view Consensus   Self-supervised detection and segmentation of foreground objects aims for
accuracy without annotated training data. However, existing approaches
predominantly rely on restrictive assumptions on appearance and motion. For
scenes with dynamic activities and camera motion, we propose a multi-camera
framework in which geometric constraints are embedded in the form of multi-view
consistency during training via coarse 3D localization in a voxel grid and
fine-grained offset regression. In this manner, we learn a joint distribution
of proposals over multiple views. At inference time, our method operates on
single RGB images. We outperform state-of-the-art techniques both on images
that visually depart from those of standard benchmarks and on those of the
classical Human3.6M dataset.

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The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex