# Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt

arXiv: https://arxiv.org/abs/2509.01704 · HTML: https://mlsanity.com/paper/2509.01704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)

## Abstract (arXiv)

Accurate classification of rock sizes is a vital component in geotechnical engineering, mining, and resource management, where precise estimation influences operational efficiency and safety. In this paper, we propose an enhanced deep learning model based on the ConvNeXt architecture, augmented with both self-attention and channel attention mechanisms. Building upon the foundation of ConvNext, our proposed model, termed CNSCA, introduces self-attention to capture long-range spatial dependencies and channel attention to emphasize informative feature channels. This hybrid design enables the model to effectively capture both fine-grained local patterns and broader contextual relationships within rock imagery, leading to improved classification accuracy and robustness. We evaluate our model on a rock size classification dataset and compare it against three strong baseline. The results demonstrate that the incorporation of attention mechanisms significantly enhances the models capability for fine-grained classification tasks involving natural textures like rocks.