Closed-Form Approximate CRF Training for Scalable Image Segmentation
By Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari, Christoph H. Lampert. 2014
15 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Simon Lacoste-Julien
- Jean-Baptiste Alayrac
- Puneet Kumar Dokania
- Anton Osokin
- Vladimir Kolmogorov
- Neel Shah
- Isabella Lukasewitz
- Christoph H. Lampert
In Computer Vision and Pattern Recognition
- Cordelia Schmid
- Luc Van Gool
- Andrew Zisserman
- Dacheng Tao
- Bernt Schiele
- Simon Lacoste-Julien
- Ivan Laptev
- Alan L. Yuille
Citations in the corpus
- Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016, methodology): “We build our model based on the one by Kolesnikov et al. (2014) for HorseSeg, and the one by Chen & Yuille (2014) for LSP.”
- A multi-plane block-coordinate frank-wolfe algorithm for training structural SVMs with a costly max-oracle (2014, methodology): “fficiently using dynamic programming, namely by the Viterbi algorithm [27]. Image Segmentation – HorseSeg Dataset and Stanford Backgrounds Dataset We use a subset of 2376 image of the HorseSeg dataset [17],8 and the 715 images of the Stanford Backgrounds dataset [11],9 respectively. First, each image”