# Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups

arXiv: https://arxiv.org/abs/2506.12712 · HTML: https://mlsanity.com/paper/2506.12712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [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)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [Visual Attention Network](https://mlsanity.com/paper/2202.09741/) (2022)
- [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://mlsanity.com/paper/2201.09450/) (2022)
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- [Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks](https://mlsanity.com/paper/1810.12348/) (2018)
- [Is Attention Better Than Matrix Decomposition?](https://mlsanity.com/paper/2109.04553/) (2021)
- [A Hierarchy of Limitations in Machine Learning](https://mlsanity.com/paper/2002.05193/) (2020)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [Blockchain Systems, Technologies and Applications: A Methodology Perspective](https://mlsanity.com/paper/2105.03572/) (2021)

## Abstract (arXiv)

The segmentation of coal maceral groups can be described as a semantic segmentation process of coal maceral group images, which is of great significance for studying the chemical properties of coal. Generally, existing semantic segmentation models of coal maceral groups use the method of stacking parameters to achieve higher accuracy. It leads to increased computational requirements and impacts model training efficiency. At the same time, due to the professionalism and diversity of coal maceral group images sampling, obtaining the number of samples for model training requires a long time and professional personnel operation. To address these issues, We have innovatively developed an IoT-based DA-VIT parallel network model. By utilizing this model, we can continuously broaden the dataset through IoT and achieving sustained improvement in the accuracy of coal maceral groups segmentation. Besides, we decouple the parallel network from the backbone network to ensure the normal using of the backbone network during model data updates. Secondly, DCSA mechanism of DA-VIT is introduced to enhance the local feature information of coal microscopic images. This DCSA can decompose the large kernels of convolutional attention into multiple scales and reduce 81.18% of parameters.Finally, we performed the contrast experiment and ablation experiment between DA-VIT and state-of-the-art methods at lots of evaluation metrics. Experimental results show that DA-VIT-Base achieves 92.14% pixel accuracy and 63.18% mIoU. Params and FLOPs of DA-VIT-Tiny are 4.95M and 8.99G, respectively. All of the evaluation metrics of the proposed DA-VIT are better than other state-of-the-art methods.