Mapping Natural Image Patches by Explicit and Implicit Manifolds

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How to Represent Part-Whole Hierarchies in a Neural Network (2021, background): “This is a much more ﬂexible way of eliminating redundancy [Shi and Zhu, 2007].”
Evaluating information contributions of bottom-up and top-down processes (2009, methodology): “Based on the labeled perfect sketches and region segmentations, the frequency of the five nodes are counted in training dataset using the method in [6, 20], as shown in Fig.”
I2T: Image Parsing to Text Description (2010, methodology): “It is also worth mentioning that primitives are not explicitly annotated in the data set; they are clustered from the annotated data using the method proposed in [63].”
Video Primal Sketch: A Unified Middle-Level Representation for Video (2015, methodology): “For example, dynamic textures (Szummer and Picard (1996); Campbell et al (2002)) were modeled by a spatiotemporal auto-regressive (STAR) model, in which the intensity of each pixel was represented by a linear summation of intensities of its spatial and temporal neighbors.”
Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015, background): “Especially in [48], Shi and Zhu characterize the image patches by explicit manifold and implicit manifold.”