Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013, background): “pects of a given domain through discovery of salient clusters, parts, mid-level features, and/or hidden units (Hinton & Salakhutdinov, 2006; Fidler & Leonardis, 2007; Zhu et al., 2007; Singh et al., 2012; Krizhevsky et al., 2012).”
A stochastic graph grammar for compositional object representation and recognition (2009, background): “The further grammar works on object recognition are presented by Lin [17] and Zhu [22].”
Max Margin AND/OR Graph learning for parsing the human body (2008, background): “They are defined by ΨH ν (y )= g(zµ ,z ρ ,z τ ), ∀ν ∈ V AN D(t) where g(., ., .) is a logarithm of Gaussian distribution defined on the invariant shape vector l(zµ ,z ρ ,z τ ) [ 25 ] constructed from triple child nodes (zµ ,z ρ ,z τ ) of node ν. This shape vector depends only on variablesofthe tripl”
Learning Semantic Image Representations at a Large Scale (2014, background): “It has long been argued that deep or layered compositional architectures should be able to capture salient aspects of a given domain through discovery of salient clusters, parts, mid-level features, and/or hidden units [48, 41, 125, 102, 65].”
Detecting Object Boundaries Using Low-, Mid-, and High-level Information (2007, methodology): “Hierarchical methods [44,45] obtain very good results but use more complex object models and require heavy inference.”