Learning Instance Segmentation by Interaction

Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik

CVPRW 2018 pp. 2042-2045

doi:10.1109/CVPRW.2018.00276 /cvprw/2018/pathak2018cvprw-learning/

Abstract

Objects are a fundamental component of visual perception. How are humans able to effortlessly reorganize their visual observations into a discrete set of objects is a question that has puzzled researchers for centuries. The Gestalt school of thought put forth the proposition that humans use similarity in color, texture and motion to group pixels into individual objects [21]. Various methods for object segmentation based on color and texture cues have been proposed [3, 6, 7, 14, 16]. These approaches are, however, known to over-segment multi-colored and textured objects.

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Text

Pathak et al. "Learning Instance Segmentation by Interaction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00276

Markdown

[Pathak et al. "Learning Instance Segmentation by Interaction." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/pathak2018cvprw-learning/) doi:10.1109/CVPRW.2018.00276

BibTeX

@inproceedings{pathak2018cvprw-learning,
  title     = {{Learning Instance Segmentation by Interaction}},
  author    = {Pathak, Deepak and Shentu, Yide and Chen, Dian and Agrawal, Pulkit and Darrell, Trevor and Levine, Sergey and Malik, Jitendra},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {2042-2045},
  doi       = {10.1109/CVPRW.2018.00276},
  url       = {https://mlanthology.org/cvprw/2018/pathak2018cvprw-learning/}
}