Dense and Low-Rank Gaussian CRFs Using Deep Embeddings

By Siddhartha Chandra, Nicolas Usunier, Iasonas Kokkinos. 2017

86 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

CCNet: Criss-Cross Attention for Semantic Segmentation (2018, methodology): “BiSeNet [35] was designed for real-time semantic segmentation.”
Non-local Neural Networks (2017, background): “The iterative mean-field inference of CRF can be turned into a recurrent network and trained [56, 42, 8, 18, 34].”
Structured Convolutional Kernel Networks for Airline Crew Scheduling (2021, neutral): “Furthermore, as in Chandra et al. (2017), using deep structured predictors may require using scaling.”
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (2018, background): “, image pyramid) [18, 16, 58, 44, 11, 9] or those that adopt probabilistic graphical models (such as DenseCRF [37] with efficient inference algorithm [2]) [8, 4, 82, 44, 48, 55, 63, 34, 72, 6, 7, 9].”
Videos as Space-Time Region Graphs (2018, methodology): “In the context of deep learning, especially for semantic segmentation, the CRF model is often applied on the outputs of the ConvNets by performing mean-field inference [61,62,63,64,65,66].”
Symbolic Graph Reasoning Meets Convolutions (2018, methodology): “Some works [51, 41, 5] thus formulated graphical models and structure constraints (e.”
Semi-convolutional Operators for Instance Segmentation (2018, methodology): “[6] connects embeddings, low rank matrices and densely connected random fields.”
PSANet: Point-wise Spatial Attention Network for Scene Parsing (2018, methodology): “Different from these extensions, conditional random field (CRF) [4,46,2,3] and Markov random field (MRF) [25] are also utilized.”
Graph-Based Global Reasoning Networks (2018, methodology): “CRFs [3] and random walk networks [1] are proposed based on the graph model for effective image segmentation.”
Non-Local Recurrent Network for Image Restoration (2018, unknown): “In addition to image restoration, feature correlations are widely exploited along with neural networks in many other areas, including graphical models [51, 4, 17], relational reasoning [32], machine translation [13, 40] and so on.”
ACFNet: Attentional Class Feature Network for Semantic Segmentation (2019, background): “Conditional random field (CRF) [6, 5, 7], Markov random field (MRF) [25] and Recurrent Neural Networks (RNNs) [4, 32] are also widely used to exploit the long-range dependencies.”