# A Deep Learning Framework for Boundary-Aware Semantic Segmentation

arXiv: https://arxiv.org/abs/2503.22050 · HTML: https://mlsanity.com/paper/2503.22050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [AdaViT: Adaptive Tokens for Efficient Vision Transformer](https://mlsanity.com/paper/2112.07658/) (2021)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [PEM: Prototype-based Efficient MaskFormer for Image Segmentation](https://mlsanity.com/paper/2402.19422/) (2024)
- [A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis](https://mlsanity.com/paper/2009.13721/) (2020)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning](https://mlsanity.com/paper/2502.09086/) (2025)
- [A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks](https://mlsanity.com/paper/2502.16484/) (2025)
- [A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization](https://mlsanity.com/paper/2503.06888/) (2025)
- [Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM](https://mlsanity.com/paper/2503.11084/) (2025)

## Built on by (most cited first, 2 of 2)

- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)
- [DeepSORT-Driven Visual Tracking Approach for Gesture Recognition in Interactive Systems](https://mlsanity.com/paper/2505.07110/) (2025)

## Abstract (arXiv)

As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysis, and medical image processing. In recent years, Transformer-based segmentation methods have demonstrated strong performance in global feature modeling. However, they still struggle with blurred target boundaries and insufficient recognition of small targets. To address these issues, this study proposes a Mask2Former-based semantic segmentation algorithm incorporating a boundary enhancement feature bridging module (BEFBM). The goal is to improve target boundary accuracy and segmentation consistency. Built upon the Mask2Former framework, this method constructs a boundary-aware feature map and introduces a feature bridging mechanism. This enables effective cross-scale feature fusion, enhancing the model's ability to focus on target boundaries. Experiments on the Cityscapes dataset demonstrate that, compared to mainstream segmentation methods, the proposed approach achieves significant improvements in metrics such as mIOU, mDICE, and mRecall. It also exhibits superior boundary retention in complex scenes. Visual analysis further confirms the model's advantages in fine-grained regions. Future research will focus on optimizing computational efficiency and exploring its potential in other high-precision segmentation tasks.