mlsanityData to 7 Oct 2026

Evidential fully convolutional network for semantic segmentation

arXiv 2103.13544 ↗First posted 25 Mar 2021cs.CV cs.AIcited by 11

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Abstract (arXiv)

We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation. In the so-called evidential FCN (E-FCN), an encoder-decoder architecture first extracts pixel-wise feature maps from an input image. A Dempster-Shafer layer then computes mass functions at each pixel location based on distances to prototypes. Finally, a utility layer performs semantic segmentation from mass functions and allows for imprecise classification of ambiguous pixels and outliers. We propose an end-to-end learning strategy for jointly updating the network parameters, which can make use of soft (imprecise) labels. Experiments using three databases (Pascal VOC 2011, MIT-scene Parsing and SIFT Flow) show that the proposed combination improves the accuracy and calibration of semantic segmentation by assigning confusing pixels to multi-class sets.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

12 of 49 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2017On Calibration of Modern Neural Networkscited by 4,860
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  7. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  8. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  9. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  10. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365
  11. 2018Decision-Making with Belief Functions: a Reviewcited by 13
  12. 2018Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspectivecited by 11

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Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 1; 2024: 2.

11 papers.

  1. 2022Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentationcited by 20
  2. 2022Lymphoma segmentation from 3D PET-CT images using a deep evidential networkcited by 12
  3. 2021Evidential segmentation of 3D PET/CT imagescited by 8
  4. 2022An Evidential Neural Network Model for Regression Based on Random Fuzzy Numberscited by 6
  5. 2024An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentationcited by 5
  6. 2021Fusion of evidential CNN classifiers for image classificationcited by 2
  7. 2022Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentationcited by 2
  8. 2023Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentationcited by 2
  9. 2024DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentationcited by 1
  10. 2022A comparative study of attention mechanism and generative adversarial network in facade damage segmentationcited by 0
  11. 2022IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networkscited by 0