Toward Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

By Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen. 2012

89 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning human activities and object affordances from RGB-D videos (2012, methodology): “There is some recent work in interpreting human actions and interaction with objects (Lopes and Santos-Victor, 2005; Saxena et al., 2008; Aksoy et al., 2011; Konidaris et al., 2012; Li et al., 2012) in context of learning to perform actions from demonstrations.”
Relating Things and Stuff via ObjectProperty Interactions (2014, methodology): “inference procedures which leverage existing approaches for detection and segmentation and use the output of such approaches as input features in an iterative fashion [23], [24], [25], [26].”
Semantic Image Segmentation with Contextual Hierarchical Models (2015, methodology): “A common approach is to use a series of cascaded classifiers [3], [5], [6], [7].”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “In [175], a feedback enabled cascaded classification model was developed to jointly optimize several subtasks in scene understanding.”
Learning to place new objects in a scene (2012, methodology): “For example, using estimated 3D geometric properties from images can be useful for object detection (Saxena et al., 2008a, 2009b; Heitz et al., 2008; Li et al., 2010; Hedau et al., 2009; Divvala et al., 2009).”
Depth Extraction from Video Using Non-parametric Sampling (2012, background): “187 N/A Feedback Cascades [8] N/A N/A 15.”
Semi-Supervised Deep Learning for Monocular Depth Map Prediction (2017, background): “Further lines of research in supervised training of depth map prediction use the idea of depth transfer from example images [13, 12, 21], or integrate depth map prediction with semantic segmentation [15, 19, 4, 26, 18].”
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs (2013, background): “Recent works [12, 40, 16, 23], have thus pushed on holistic scene understanding models for among other things, improved semantic segmentation.”
Contextually guided semantic labeling and search for three-dimensional point clouds (2013, methodology): “In the past, a significant amount of work has been done in semantic labeling of 2D images (Murphy et al., 2003; Felzenszwalb et al., 2008; Heitz and Koller, 2008; Collet et al., 2011; Li et al., 2012).”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Relating Things and Stuff via Object Property Interactions (unknown): “To overcome this limitation, some authors have proposed inference procedures which leverage existing approaches for detection and segmentation and use the output of such approaches as input features in an iterative fashion [23]–[26].”
DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding (2016, background): “The role of context has been studied extensively in computer vision [27, 41, 5, 4, 3, 7, 18, 37, 33, 35, 34, 19, 12, 9, 43, 1, 39, 24, 10, 42, 20, 21].”