Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep Networks

By Carlo Gatta, Adriana Romero, Joost van de Weijer. 2014

38 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (2016, methodology): “First, different alternatives have been proposed in the literature to address the resolution recovery in FCN’s upsampling path; from simple bilinear interpolation [10, 20, 1] to more sophisticated operators such as unpooling [1, 21] or transposed convolutions [20].”
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2015, methodology): “been attempts to apply networks designed for object categorization to segmentation, particularly by replicating the deepest layer features in blocks to match image dimensions [6], [36], [37], [38].”
Region-Based Semantic Segmentation with End-to-End Training (2016, unknown): “Byeon [41] 2015 22.6 68.7 Gould [45] 2014 25.7 78.4 Tighe [13] 2010 29.4 76.9 Pinheiro [25] 2014 30.0 76.5 Gatta [46] 2014 32.1 78.7 Singh [47] 2013 33.8 79.2 Shuai [42] 2015 39.7 80.1 Tighe [14] 2013”
ReSeg: A Recurrent Neural Network for Object Segmentation (2015, background): “Recently however many approaches in this direction have been explored (see, e.g., Graves & Schmidhuber, 2009; Grangier et al., 2009; Farabet et al., 2013; Hariharan et al., 2014; Long et al., 2014; Ronneberger et al., 2015; Pinheiro & Collobert, 2014; Gatta et al., 2014; Visin et al., 2015; Kalchbre”
Integrating parametric and non-parametric models for scene labeling (2015, background): “4The author of [5] reports the training/testing time of different CNNs by personal communication to the original authors.”
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling (2015, background): “There have also been a few attempts to apply networks designed for categorization to segmentation, particularly by replicating the deepest layer features in blocks to match image dimensions [7, 6, 11, 8].”
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation (2015, methodology): “However, these methods are not designed to take into account and preserve both local and global contextual dependencies, which has shown to be useful for semantic segmentation tasks [40, 17].”