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

Divam Gupta

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

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arxiv 2307.13215 v1 pith:3NJFA5SI submitted 2023-07-25 cs.CV

Divam Gupta This is my paper

classification cs.CV

keywords segmentationmodelskeraspspnetsegnetsemanticunetchallenges

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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features
cs.CV 2025-05 reject novelty 4.0 of 10

A pipeline that combines geometric-mean Grad-CAM maps with pre-trained segmentation masks to locate manipulations without pixel labels, tested on a small biased subset of CASIA2.0.

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