mlsanityData to 14 Oct 2026

Semi-Supervised Representative Region Texture Extraction of Faccade

arXiv 2212.02220 ↗First posted 5 Dec 2022cs.CVcited by 0

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Abstract (arXiv)

Researches of analysis and parsing around faccades to enrich the 3D feature of faccade models by semantic information raised some attention in the community, whose main idea is to generate higher resolution components with similar shapes and textures to increase the overall resolution at the expense of reconstruction accuracy. While this approach works well for components like windows and doors, there is no solution for faccade background at present. In this paper, we introduce the concept of representative region texture, which can be used in the above modeling approach by tiling the representative texture around the faccade region, and propose a semi-supervised way to do representative region texture extraction from a faccade image. Our method does not require any additional labelled data to train as long as the semantic information is given, while a traditional end-to-end model requires plenty of data to increase its performance. Our method can extract texture from any repetitive images, not just faccade, which is not capable in an end-to-end model as it relies on the distribution of training set. Clustering with weighted distance is introduced to further increase the robustness to noise or an imprecise segmentation, and make the extracted texture have a higher resolution and more suitable for tiling. We verify our method on various faccade images, and the result shows our method has a significant performance improvement compared to only a random crop on faccade. We also demonstrate some application scenarios and proposed a faccade modeling workflow with the representative region texture, which has a better visual resolution for a regular faccade.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 60 references that are in the map. First posted on arXiv 5 Dec 2022.

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32 of 60 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  6. 2017Mask R-CNNcited by 7,277
  7. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  8. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2014Going Deeper with Convolutionscited by 4,241
  11. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  12. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
20 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  2. 2013Describing Textures in the Wildcited by 2,125
  3. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  4. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  5. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  6. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  7. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  8. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 734
  9. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 673
  10. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
  11. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 504
  12. 2019Revisiting Self-Supervised Visual Representation Learningcited by 251
  13. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 174
  14. 2014PCANet: A Simple Deep Learning Baseline for Image Classification?cited by 70
  15. 2016Deep TEN: Texture Encoding Networkcited by 44
  16. 2013Some Improvements on Deep Convolutional Neural Network Based Image Classificationcited by 34
  17. 2020Structure-Aware Completion of Photogrammetric Meshes in Urban Road Environmentcited by 7
  18. 2020Fast and Regularized Reconstruction of Building Faccades from Street-View Images using Binary Integer Programmingcited by 6
  19. 2022Self-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Imagescited by 5
  20. 2022Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Faccade Modelingcited by 3

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