Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

By Golnaz Ghiasi, Charless C. Fowlkes. 2016

466 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Feature Pyramid Networks for Object Detection (2016, background): “[8] present a Laplacian pyramid presentation for FCNs to progressively refine segmentation.”
Deep Watershed Transform for Instance Segmentation (2016, methodology): “(a) Input Image (b) Semantic Segmentation [9] (c) Our Instance Segmentation (d) GT Instance Segmentation Figure 1: Sample prediction: the input image is gated by sem. segmentation from [9] and passed through our model.”
InstanceCut: From Edges to Instances with MultiCut (2016, methodology): “For the semantic segmentation block in our framework we test two different networks, which have publicly available trained models for CityScapes: Dila-tion10 [52] and LRR-4x [20].”
Pyramid Scene Parsing Network (2016, methodology): “For fair comparison with current ResNet based frameworks [38, 9, 4] in scene parsing/semantic segmentation task, we build our architecture based on ResNet101 while without postprocessing like CRF.”
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network (2017, methodology): “…[9] 71.0 DeepLab-MSc-CRF-LargeFOV [6] 71.6 Oxford TVG CRF RNN COCO [37] 74.7 CUHK DPN COCO [24] 77.5 Oxford TVG HO CRF [2] 77.9 CASIA IVA OASeg [33] 78.3 Adelaide VeryDeep FCN VOC [34] 79.1 LRR 4x ResNet COCO [12] 79.3 Deeplabv2-CRF [7] 79.7 CentraleSupelec Deep G-CRF[5] 80.2 Our approach 82.2”
Modeling the Background for Incremental Learning in Semantic Segmentation (2020, methodology): “Neural Networks [20, 2] and use different strategies to condition pixel-level annotations on their global context, e.g . using multiple scales [38, 19, 6, 5, 37, 8] and/or modeling spatial dependencies [6, 12].”
OctNet: Learning Deep 3D Representations at High Resolutions (2016, background): “We will focus on the most common operations in convolutional networks [13, 18, 35]: convolution, pooling and unpooling.”
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation (2016, methodology): “The evaluation results of these variants on the NYUD dataset are shown in Table 9.”
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition (2016, methodology): “LRR [9] also uses the coarse set for training.”
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (2017, methodology): “The Laplacian pyramid has been used in a wide range of applications, such as image blending [4], texture synthesis [14], edge-aware filtering [24] and semantic segmentation [11, 25].”
ExFuse: Enhancing Feature Fusion for Semantic Segmentation (2018, unknown): “We use two public-available semantic segmentation benchmarks – PASCAL VOC 2012 [20] and Semantic Boundaries Dataset [29] – for training and evaluate performances on PASCAL VOC 2012 validation set, which is consistent with many previous work [1,2,3,4,5,6,8,9,10,13,11,12,27].”
Recurrent Pixel Embedding for Instance Grouping (2017, methodology): “This partition into figure/boundary/background is sometimes referred to as a tri-map in the matting literature and has been previously utilized in analyzing semantic segmentation performance [42, 10, 27].”
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation (2018, methodology): “U-Shape method: The U-shape structure [1,10,22,24,27] can recover a certain extent of spatial information.”
Image Segmentation Using Deep Learning: A Survey (2020, unknown): “Ghiasi and Fowlkes [55] developed a multiresolution”
PSANet: Point-wise Spatial Attention Network for Scene Parsing (2018, methodology): “LRR [11] and RefineNet [21] adopted step-wise reconstruction and refinement to get parsing results.”