Abstract
Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and manufacturing, where many of these applications are plagued with scarce labeled data and low in situ computational resources. While recent image segmentation studies focus on segmentation mask alignment with ground-truth, the task of boundary detection remains understudied, especially in the low data regime. In this work, we present a lightweight discrete diffusion contour refinement pipeline for robust boundary detection in the low data regime. We use a Convolutional Neural Network(CNN) architecture with self-attention layers as the core of our pipeline, and condition on a segmentation mask, iteratively denoising a sparse contour representation. We introduce multiple novel adaptations for improved low-data efficacy and inference efficiency, including using a simplified diffusion process, a customized model architecture, and minimal post processing to produce a dense, isolated contour given a dataset of size <500 training images. Our method outperforms several SOTA baselines on the medical imaging dataset KVASIR, is competitive on HAM10K and our custom wildfire dataset, Smoke, while improving inference framerate by 3.5X.
Abstract (translated)
不规则和透明物体的边界检测是一个重要的问题，它在医学成像、环境监测及制造业中有着广泛的应用。然而，在这些领域中，许多应用由于缺乏标注数据且现场计算资源有限而面临挑战。尽管最近的图像分割研究主要集中在与地面真实值的分割掩码对齐上，但低数据环境下边界检测的任务仍然较少被探索。在这项工作中，我们提出了一种轻量级离散扩散轮廓细化流水线，用于在低数据情况下进行稳健的边界检测。 我们的方法采用带有自注意力层的卷积神经网络（CNN）架构作为核心，并基于分割掩码条件，迭代地去噪稀疏轮廓表示。为了提高低数据下的有效性和推理效率，我们引入了多种新颖的适应性改进措施，包括简化扩散过程、定制模型架构以及最小化后处理步骤，在仅使用少于500张训练图像的数据集上产生稠密且独立的轮廓。 在医学成像数据集KVASIR上，我们的方法优于多个最先进的基线。同时，在HAM10K和我们自定义的野火数据集Smoke上的性能也具有竞争力，并且推理帧率提高了3.5倍。
URL
https://arxiv.org/abs/2602.05880
