Shape discovery from unlabeled image collections

By Yong Jae Lee, Kristen Grauman. 2009

78 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks (2019, methodology): “Related work from computer vision includes (Cao and Fei-Fei 2007; Kim and Torralba 2009; Lee and Grauman 2009; Russell et al. 2006; Karpathy, Miller, and Fei-Fei 2013), as well as work on co-segmentation (Zhu et al. 2016) and unsupervised object recognition (Tuytelaars et al. 2010).”
Advanced Structured Prediction (2014, methodology): “After linking all pairs of edges and computing their dissimilarities, Page Rank (Brin and Page, 1998; Kim et al., 2008; Lee and Grauman, 2009) iteratively estimates the degree of repetition of each edge as ψi2 = (1− ρ) + ρ ∑”
An Exemplar-Based CRF for Multi-instance Object Segmentation (2014, methodology): “Our method can be used to discover new shape masks in a large dataset [14].”
Unsupervised object class discovery via saliency-guided multiple class learning (2012, methodology): “As stated before, several unsupervised approaches have recently been proposed for the object localization and categorization task [26, 15, 22, 18, 20, 38, 19, 32, 21].”
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models (2013, methodology): “[29] manually cropped and aligned target objects in images for training, whereas [30, 25, 31] used unsupervised segmentation to generate object candidates, which relied on the foreground-background discrimination.”
Weakly Supervised Localization and Learning with Generic Knowledge (2012, unknown): “…class (target) is helped by labeled examples of other related classes (sources) [De-selaers et al, 2010, Fei-Fei et al, 2004, Lampert et al, 2009a, Lando and Edelman, 1995, Quattoni et al, 2008, Rohrbach et al, 2010, Stark et al, 2009, Tommasi and Caputo, 2009, Tommasi et al, 2010, Torresani et…”
Mining And-Or Graphs for Graph Matching and Object Discovery (2015, background): “As in object-discovery work [16, 44], such an object collection was understood as a clustering of unannotated objects, and the clustering purity was chosen as the evaluation metric.”
From a Set of Shapes to Object Discovery (2010, methodology): “For all three graphs, we apply the standard PageRank algorithm, also used in [9,17,10], to identify the most relevant contours, which are then interpreted as object contours.”