GrabCut in One Cut
By Meng Tang, Lena Gorelick, Olga Veksler, Yuri Boykov. 2013
239 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
- Luc Van Gool
- Ming-Hsuan Yang
- Ming-Ming Cheng
- Philip H. S. Torr
- Xiaogang Wang
- Dacheng Tao
- Bernt Schiele
- Jiashi Feng
Citations in the corpus
- Deep Extreme Cut: From Extreme Points to Object Segmentation (2017, neutral): “GrabCut [32] 8.1 KernelCut [36] 7.1 OneCut [37] 6.7 Extreme clicks [24] 5.5 BoxPrior [18] 3.7 MILCut [38] 3.6 DeepGC [40] 3.4 Ours 2.3”
- Global Contrast Based Salient Region Detection (2015, methodology): “Although several new methods [53], [54] have been developed since the initial version of this work [1], to the best of our knowledge, our salient object segmentation results are still the best results reported on the most widely used benchmark [33].”
- Deep GrabCut for Object Selection (2017, methodology): “Improvements to this method include optimizing the segmentation and color models in one step [22] and adding a spatial extent prior [12].”
- Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-Modal Pretraining (2021, methodology): “We first use GrabCut [28] to obtain the foreground masks of single-product images.”
- Loosecut: Interactive image segmentation with loosely bounded boxes (2015, methodology): “Also using an MRF model, OneCut [17] is recently developed for interactive image segmentation.”
- DenseCut: Densely Connected CRFs for Realtime GrabCut (2015, methodology): “However, as in [TGVB13], ignoring this ratio constraint gives us good results in practice.”
- Deepstrip: High-Resolution Boundary Refinement (2020, background): “Recent decades have witnessed considerable effort to improve the boundary quality of an object that has been detected [41, 35, 32, 18, 37, 47, 16, 22] or segmented [11, 34, 24, 7].”
- Interactive Image Segmentation via Backpropagating Refinement Scheme (2019, background): “[49] define a cost function that can be minimized in a single pass of graph-cut optimization.”