Superparsing

By Joseph Tighe, Svetlana Lazebnik. 2010

674 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Fully convolutional networks for semantic segmentation (2014, neutral): “Tighe [33] is a non-parametric transfer method.”
Convolutional feature masking for joint object and stuff segmentation (2014, methodology): “We compare with two leading methods SuperParsing [21] and O2P [4], whose results are reported in [18].”
COCO-Stuff: Thing and Stuff Classes in Context (2016, methodology): “More recent large-scale datasets like Barcelona [50], LM+SUN [52], PASCAL Context [38], Cityscapes [13] and ADE20K [63] annotate tens of thousands of images with hundreds of classes.”
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture (2014, methodology): “nds to produce large constant regions with considerable accuracy. 6.3.2 Sift Flow We also conﬁrm our method can be applied to additional scene types by evaluating performance on the Sift Flow dataset [28], which contains images of outdoor cityscapes and landscapes segmented into 33 categories. All i”
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2015, methodology): “Another approach argues for the use of a combination of popular hand designed features and spatio-temporal super-pixelization to obtain higher accuracy [33].”
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, background): “One of these is nonparametric image parsing (Liu et al., 2010; Tighe and Lazebnik, 2010) where, given a query image, a small number of similar training images is retrieved and labels are transferred from these images to the query.”
Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “Traditional semantic segmentation algorithms use handengineered extracted features which are fed into a classifier (Shotton, Johnson, and Cipolla 2008; Tighe and Lazebnik 2010), where the classifier is trained using supervised learning.”
Recurrent Convolutional Neural Networks for Scene Parsing (2013, neutral): “Tighe & Lazebnik, 2010 [3] Global, shape, location, texture/SIFT, color, appearance, MRF.”
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts (2016, methodology): “The various scene layouts that we demonstrate on Figure 3 depict various scene types, e.g. urban, mountain, forest, sea, lake, and they correspond to im- ages from SIFTflow [15] and LMSun [24] datasets to insure that neither the original images nor the semantic layouts are previously observed while ”
Parsing the Hand in Depth Images (2014, methodology): “However, unlike [38] in which a superpixel in the color images is equally affected by all its neighbors, we take the depth discontinuity and the irregular shapes of the superpixels into consideration to model the pairwise interaction energy.”
Sensor fusion for semantic segmentation of urban scenes (2015, methodology): “For images, Tighe et al. [6] [7], Singh et al. [8], and Yang et al. [9] use an image retrieval system followed by non-parametric classification to produce superpixel classifications, with special considerations taken for objects [7], semantic context [8], and rare classes [9].”
Finding Things: Image Parsing with Regions and Per-Exemplar Detectors (2013, methodology): “We use the split of [27], which consists of 45,176 training and 500 test images.”
Recursive deep learning for natural language processing and computer vision (2014, background): “The connection between NLP ideas such as parsing or grammars and computer vision has been explored before (Zhu and Zhang, 2006; Tighe and Lazebnik, 2010), among many others.”