Abstract
Deep learning models have achieved significant success in various image related tasks. However, they often encounter challenges related to computational complexity and overfitting. In this paper, we propose an efficient approach that leverages polygonal representations of images using dominant points or contour coordinates. By transforming input images into these compact forms, our method significantly reduces computational requirements, accelerates training, and conserves resources making it suitable for real time and resource constrained applications. These representations inherently capture essential image features while filtering noise, providing a natural regularization effect that mitigates overfitting. The resulting lightweight models achieve performance comparable to state of the art methods using full resolution images while enabling deployment on edge devices. Extensive experiments on benchmark datasets validate the effectiveness of our approach in reducing complexity, improving generalization, and facilitating edge computing applications. This work demonstrates the potential of polygonal representations in advancing efficient and scalable deep learning solutions for real world scenarios. The code for the experiments of the paper is provided in this https URL.
Abstract (translated)
深度学习模型在各种与图像相关的任务中取得了显著的成功。然而，它们经常遇到计算复杂性和过拟合的挑战。在这篇论文中，我们提出了一种有效的方法，利用图像的多边形表示（使用主导点或轮廓坐标）。通过将输入图像转换为这些紧凑形式，我们的方法可以显着减少计算需求、加速训练并节省资源，使其适用于实时和资源受限的应用程序。这些表示自然地捕获了关键的图像特征，并过滤掉了噪声，提供了一种天然的正则化效应以减轻过拟合问题。所得到的轻量级模型在性能上与使用全分辨率图像的最佳方法相当，同时能够在边缘设备上部署。在基准数据集上的广泛实验验证了我们方法在降低复杂性、改进泛化和促进边缘计算应用方面的有效性。这项工作展示了多边形表示在为现实世界场景推进高效且可扩展的深度学习解决方案方面所具有的潜力。论文实验的代码可在以下网址获取：[提供的URL]。
URL
https://arxiv.org/abs/2504.01214
