mlsanityData to 14 Oct 2026

Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras

arXiv 2307.13215 ↗First posted 25 Jul 2023cs.CVcited by 3

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Abstract (arXiv)

Semantic segmentation plays a vital role in computer vision tasks, enabling precise pixel-level understanding of images. In this paper, we present a comprehensive library for semantic segmentation, which contains implementations of popular segmentation models like SegNet, FCN, UNet, and PSPNet. We also evaluate and compare these models on several datasets, offering researchers and practitioners a powerful toolset for tackling diverse segmentation challenges.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

8 of 10 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2016Pyramid Scene Parsing Networkcited by 2,479
  7. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  8. 2020Semantic Segmentation of Underwater Imagery: Dataset and Benchmarkcited by 95

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024P-Mamba: Marrying Perona Malik Diffusion with Mamba for Efficient Pediatric Echocardiographic Left Ventricular Segmentationcited by 21
  2. 2024A Cranial-Feature-Based Registration Scheme for Robotic Micromanipulation Using a Microscopic Stereo Camera Systemcited by 0
  3. 2025Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Featurescited by 0