mlsanityData to 14 Oct 2026

FoR-Net: Focus-on-Regions Network for Semantic Segmentation

arXiv 2605.02764 ↗First posted 4 May 2026cs.CVcited by 0

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Abstract (arXiv)

This paper presents Focus-on-Regions Network (FoR-Net), an efficient semantic segmentation framework that explicitly focuses on hard regions through a selector-driven Top-K mechanism. Instead of relying on heavy global modeling, FoR-Net selectively enhances structurally informative regions. Multi-scale reasoning branches are introduced to aggregate spatial context efficiently. Experiments on the Cityscapes dataset demonstrate that FoR-Net achieves competitive performance while maintaining a lightweight architecture.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 17 references that are in the map. First posted on arXiv 4 May 2026.

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References · 16

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  5. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  7. 2016Pyramid Scene Parsing Networkcited by 2,479
  8. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  9. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
  10. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,271
  11. 2018Dual Attention Network for Scene Segmentationcited by 1,003
  12. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 718
4 more
  1. 2019Segmentation Transformer: Object-Contextual Representations for Semantic Segmentationcited by 576
  2. 2019Asymmetric Non-local Neural Networks for Semantic Segmentationcited by 196
  3. 2020Disentangled Non-Local Neural Networkscited by 132
  4. 2026Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentationcited by 2

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