ML Anthology

Learning to Segment Affordances

Timo Lüddecke, Florentin Wörgötter
ICCVW 2017 pp. 769-776
doi:10.1109/ICCVW.2017.96 /iccvw/2017/luddecke2017iccvw-learning/

Abstract

The goal of this work is to densely predict a comparatively large set of affordances given only single RGB images. We approach this task by using a convolutional neural network based on the well-known ResNet architecture, which we blend with refinement modules recently proposed in the semantic segmentation literature. A novel cost function, capable of handling incomplete data, is introduced, which is necessary because we make use of segmentations of objects and their parts to generate affordance maps. We demonstrate both, quantitatively and qualitatively, that learning a dense predictor of affordances from an object part dataset is indeed possible and show that our model outperforms several baselines.

ICCVW Semantic Scholar

Cite

Text

Lüddecke and Wörgötter. "Learning to Segment Affordances." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.96

Markdown

[Lüddecke and Wörgötter. "Learning to Segment Affordances." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/luddecke2017iccvw-learning/) doi:10.1109/ICCVW.2017.96

BibTeX

@inproceedings{luddecke2017iccvw-learning,
  title     = {{Learning to Segment Affordances}},
  author    = {Lüddecke, Timo and Wörgötter, Florentin},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {769-776},
  doi       = {10.1109/ICCVW.2017.96},
  url       = {https://mlanthology.org/iccvw/2017/luddecke2017iccvw-learning/}
}