Nonparametric higher-order learning for interactive segmentation
By Tae Hoon Kim, Kyoung Mu Lee, Sang Uk Lee. 2010
86 citations recorded by the source corpus.
Subfields: 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 Computer Vision and Pattern Recognition
- Ming-Ming Cheng
- Luc Van Gool
- Ming-Hsuan Yang
- Alan L. Yuille
- Chunhua Shen
- Andrew Zisserman
- Zheng Lin
- Ling Shao
Citations in the corpus
- FocalClick: Towards Practical Interactive Image Segmentation (2022, background): “Before the era of deep learning, researchers [11, 12, 19, 34] take interactive segmentation as an optimization procedure.”
- Conditional Diffusion for Interactive Segmentation (2021, methodology): “[12] proposes a high order method with the constrain of label consistency.”
- Learning full pairwise affinities for spectral segmentation (2010, methodology): “Multilayer model: In [16], [17], the multilayer graph was designed.”
- Interactive Image Segmentation With First Click Attention (2020, background): “[27] also introduced a new higher-order formulation, additionally imposing the soft label consistency constraint.”
- TouchCut: Fast image and video segmentation using single-touch interaction (2014, background): “…(Tinghuai Wang), han bo@outlook.com (Bo Han), j.collomosse@surrey.ac.uk (John Collomosse) Preprint submitted to Computer Vision and Image Understanding July 22, 2013 is generally regarded as under-constrained since, in the absence of high level scene understanding, there can be…”
- Interactive Image Segmentation via Backpropagating Refinement Scheme (2019, background): “[22] generate various segmentation maps for an image, by employing different parameters, and then encourage pixels within a segment to have the same label in the final result.”
- Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation (2023, unknown): “Interactive segmentation has a rich history, initially regarded as an optimization technique by researchers (Grady 2006; Gulshan et al. 2010; Kim, Lee, and Lee 2010; Rother, Kolmogorov, and Blake 2004).”
- F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation (2020, background): “In [16], several segmentation maps are first generated for an image.”