Depth-from-Recognition: Inferring Meta-Data by Cognitive Feedback

Alexander Thomas, Vittorio Ferrari, Bastian Leibe, Tinne Tuytelaars, Luc Van Gool

ICCV 2007 pp. 1-8

doi:10.1109/ICCV.2007.4408831 /iccv/2007/thomas2007iccv-depth/

Abstract

Thanks to recent progress in category-level object recognition, we have now come to a point where these techniques have gained sufficient maturity and accuracy to succesfully feed back their output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer meta-data such as depth cues, 3D points, or object decomposition in images of previously unseen object instances. Our approach builds on the implicit shape model of Leibe and Schiele, and extends it to transfer annotations from training images to test images. Experimental results validate the viability of our approach.

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Text

Thomas et al. "Depth-from-Recognition: Inferring Meta-Data by Cognitive Feedback." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4408831

Markdown

[Thomas et al. "Depth-from-Recognition: Inferring Meta-Data by Cognitive Feedback." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/thomas2007iccv-depth/) doi:10.1109/ICCV.2007.4408831

BibTeX

@inproceedings{thomas2007iccv-depth,
  title     = {{Depth-from-Recognition: Inferring Meta-Data by Cognitive Feedback}},
  author    = {Thomas, Alexander and Ferrari, Vittorio and Leibe, Bastian and Tuytelaars, Tinne and Van Gool, Luc},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2007},
  pages     = {1-8},
  doi       = {10.1109/ICCV.2007.4408831},
  url       = {https://mlanthology.org/iccv/2007/thomas2007iccv-depth/}
}