DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection

By Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang. 2015

565 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

EDTER: Edge Detection with Transformer (2022, methodology): “Recently, convolutional neural networks (CNNs) have been successfully introduced the edge detection study [3, 4, 11, 12, 26, 29, 40, 46, 48, 52, 66].”
Holistically-Nested Edge Detection (2015, methodology): “wave of development using Convolutional Neural Networks that emphasize the importance of automatic hierarchical feature learning, including N 4-Fields (Ganin and Lempitsky 2014), DeepContour (Shen et al. 2015), DeepEdge (Bertasius et al.”
Learning Representations for Automatic Colorization (2016, methodology): “Motivating this strategy is the success of similar architectures for semantic segmentation [4,10,14,23,27] and edge detection [1,11,25,34,43].”
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (2016, neutral): “We compare our method to some of the best-established methods of boundary detection [1, 23], as well as more recent, deep learning-based ones [6,28,41,48,49,90,107].”
Adversarial Examples for Semantic Segmentation and Object Detection (2017, methodology): “Loss function During the training of the models that achieved the best results reported in this paper, we used the standard softmax loss function, that is, the log-likelihood error function described in [30].”
Harmonic Networks: Deep Translation and Rotation Equivariance (2016, neutral): “769 DeepContour†, [28] 0.”
Deep Layer Aggregation (2017, neutral): “s 68.6 DLA-102 2s 71.0 Table 5: Evaluation on CamVid. Higher depth and resolution help. DLA is state-of-the-art. Method ODS OIS AP SE [10] 0.746 0.767 0.803 DeepEdge [3] 0.753 0.772 0.807 DeepContour [36] 0.756 0.773 0.797 HED [42] 0.788 0.808 0.840 CEDN [43]y 0.788 0.804 0.821 UberNet [21] (1-Task)”
Pushing the Boundaries of Boundary Detection using Deep Learning (2015, neutral): “Method ODS OIS AP gPb-owt-ucm (Arbelaez et al., 2011) 0.726 0.757 0.696 SE-Var (Doll´ar & Zitnick, 2015) 0.746 0.767 0.803 DeepNets (Kivinen et al., 2014) 0.738 0.759 0.758 N4-Fields (Ganin & Lempitsky, 2014) 0.753 0.769 0.784 DeepEdge (Bertasius et al., 2015) 0.753 0.772 0.807 CSCNN (Hwang & Liu, 2”
Self-Supervised Visual Representation Learning from Hierarchical Grouping (2020, background): “Though deep neural networks are now the dominant tool for contour detection [41, 4, 46, 25, 47], the best prior approaches [2, 3, 11] deliver somewhat respectable results.”
SemiContour: A Semi-supervised Learning Approach for Contour Detection (2016, methodology): “[29] improve the SE contour detector [13] by replacing the widely used HoG-like features with CNN features.”
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth (2021, background): “CNN-based edge detectors [3, 4, 9, 10, 17, 20, 24, 26, 31, 36, 45] do not rely on hand-crafted features and achieve better performance.”
Learning to simplify (2016, methodology): “Related to the model we present in this paper are the approaches that output images, such as super-resolution [Dong et al. 2016], semantic segmentations [Long et al. 2015; Noh et al. 2015], contour detection [Shen et al. 2015], and optical flow [Fischer et al. 2015].”
Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning (2018, methodology): “First, outputs from edge detection algorithms, for instance the Canny edge detector [86], Matched filter [87], and Holistically-Nested Edge Detection [61], contour detectors such as, DeepEdge [62] and DeepContour [63], and/or line detectors, for example the Hough transform [64] and the Radon transfo”
Unsupervised Learning of Discriminative Attributes and Visual Representations (2016, methodology): “Figure 8 shows more of our contour detection results compared with those of Sketch Token [9] and DeepContour [10] on the BSDS500 dataset.”