Object Localization based on Structural SVM using Privileged Information

By Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han. 2014

46 citations recorded by the source corpus.

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Citations in the corpus

Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences (2017, methodology): “[11] incorporate PI into an objective function of a structural SVM to improve object localization performance.”
Recycling Privileged Learning and Distribution Matching for Fairness (2017, background): “Many variants of privileged learning methods and settings have been proposed such as, structured prediction [28], margin transfer [17], and Bayesian privileged learning [18, 29].”
Graph Distillation for Action Detection with Privileged Modalities (2017, background): “, however, the privileged information is not available, in which case the Student operates without supervision of the Teacher [55]. Several work employed privileged information (PI) on SVM classiﬁers [55,23,54,59,12]. Recently, privileged information has been applied to deep learning in various sett”
Information Bottleneck Domain Adaptation with Privileged Information for Visual Recognition (2016, methodology): “Other implementations of the LUPI paradigm include [6] for boosting, [17] for object localization in a structured prediction framework, and [47].”
Information Bottleneck Learning Using Privileged Information for Visual Recognition (2016, methodology): “The LUPI paradigm has recently been used for boosting [4], for object localization in a structured prediction framework [12], for facial feature detection [33], for metric learning [13, 46], in a logistic classification framework [43], in a max-margin latent variable model [41], and in support of do”