轻量级扩散模型提升小样本下不规则物体边界检测精度
Contour Refinement using Discrete Diffusion in Low Data Regime
- 用带自注意力的CNN结合简化扩散过程，从稀疏轮廓逐步修复出密集边界
- 在少于500张图像的数据集上仍能生成清晰孤立轮廓，且推理速度提升3.5倍
- 适用于医疗、火灾监测等标注数据稀缺场景，尤其适合算力受限设备
不规则和半透明物体的边界检测在医学成像、环境监测和制造中有重要应用，但这些场景常面临标注数据稀缺和本地计算资源有限的问题。尽管近期图像分割研究聚焦于分割掩码与真实标签对齐，边界检测尤其是低数据条件下的研究仍被忽视。本文提出一种轻量级离散扩散轮廓精修流水线，用于低数据条件下的鲁棒边界检测。采用带自注意力层的卷积神经网络作为核心，以分割掩码为条件，迭代去噪稀疏轮廓表示。引入多项新改进以提升小样本效果和推理效率，包括简化扩散过程、定制化模型架构及极简后处理，可在训练图像数小于500时生成密集、孤立轮廓。该方法在医学影像数据集KVASIR上超越多个前沿基线，在HAM10K和自建野火数据集Smoke上表现相当，同时推理帧率提升3.5倍。
原文摘要 · Abstract (English)
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.
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