Sum-product networks for modeling activities with stochastic structure

2012

76 citations recorded by the source corpus.

Citations in the corpus

• 3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks (2014, neutral): “ activity model to address the above mentioned issues, absorbing the powers of two promising techniques of broad interests: deep learning [13, 7, 11, 8, 34, 18, 32] and recongurable part-based models [41, 1, 22, 16]. We represent one human activity as a sequence of separated actions, each of which i”
• Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities (2014, unknown): “Recently, several graph-based models have been proposed to account for spatio-temporal composition of low-level features [2, 7, 8].”
• Discriminative Learning of Sum-Product Networks (2012, unknown): “Despite their tractability, SPNs are quite expressive [16], and have been used to solve difficult problems in vision [23, 1].”
• Language modeling with sum-product networks (2014, unknown): “SPNs have been successfully applied to vision problems [1, 2], but to date, no one has brought it to bear on the problem of language modeling.”
• Learning the Structure of Sum-Product Networks (2013, unknown): “In particular , they have achieved impressive results in several vision problems (Poon & Domingos, 2011; Amer & Todorovic, 2012; Gens & Domingos, 2012).”
• A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition (2015, unknown): “Our model is inspired by the effectiveness of two widely successful techniques: deep learning [21, 14, 20, 16, 48, 26, 45] and the latent structured models [56, 11, 1, 32, 24].”
• A Review of Human Activity Recognition Methods (2015, unknown): “A hier - archical structure , which is called the sum - product network , was used by Amer and Todorovic ( 2012 ) .”