Image Parsing with Stochastic Scene Grammar

By Yibiao Zhao, Song-Chun Zhu. 2011

98 citations recorded by the source corpus.

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, background): “The results that can be obtained by incorporating domain-specific knowledge are impressive (Zhao and Zhu, 2011; Del Pero et al., 2012, 2013; Tu et al., 2005; Tu and Zhu, 2002).”
Joint Video and Text Parsing for Understanding Events and Answering Queries (2013, methodology): “It has been employed in computer vision to model objects [6], scenes [7], [8] and events [9], [10].”
Picture: A probabilistic programming language for scene perception (2015, background): “Generative approaches hold out the promise of analyzing complex scenes more richly and flexibly [11, 12, 51, 7, 19, 29, 31, 16, 21], but have been less widely embraced for two main reasons: Inference typically depends on slower forms of approximate inference, and both model-building and inference ca”
Parsing IKEA Objects: Fine Pose Estimation (2013, background): “Many of these models [9, 20, 10, 6, 5, 19, 16, 7, 21] rely on gradientbased features [1, 13].”
Discovering objects and their relations from entangled scene representations (2017, background): “In computer vision, Zhu et al. (2009) and Zhao & Zhu (2011) modelled relations among image features using stochastic grammars, Felzenszwalb et al. (2008) modelled relations among object parts using the “deformable parts” model, and Choi et al. (2012) modelled relations among objects in scenes using…”
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs (2013, background): “But like traditional bottom-up pipelines for vision, these approaches have relied on considerable problem-specific engineering, chiefly to design and/or learn custom inference strategies, such as MCMC proposals [18, 22] that incorporate bottom-up cues.”
Learning 3D Semantic Scene Graphs From 3D Indoor Reconstructions (2020, background): “A line of works use probabilistic grammar to parse scenes [28, 53] or control scene synthesis [15].”
Localizing 3D cuboids in single-view images (2012, methodology): “Compared with recent approaches that detect cuboids by reasoning about the shape of the entire scene [9, 11, 12, 17, 19, 29], one of the key differences is that we detect cuboids directly without consideration of the global scene geometry.”
Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics (2013, background): “The vision communities have studied the physical properties based on single image for the ”block world” in the past three decades [3, 8, 9, 21, 15, 14]).”
Understanding Indoor Scenes Using 3D Geometric Phrases (2013, methodology): “Unlike [30], which requires a training set of hand crafted composition rules and learns only the rule parameters, our method automatically learns the set of 3DGPs from training data via our novel training algorithm (Sec.”
Scene Understanding by Reasoning Stability and Safety (2015, background): “There has also been interesting work in constructing 3D scene layouts from 2D images for indoor scenes, such as Zhao and Zhu (2011), Lee et al. (2009) (2010), and Hedau et al. (2010).”
Scene Parsing by Integrating Function, Geometry and Appearance Models (2013, methodology): “(ii) Another stream of algorithms including Han and Zhu [10], Zhao and Zhu [26] and Del Pero et al.”