Abstract
Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to efficiently generate high-quality multi-granularity edges. In EDMB, Mamba is combined with a global-local architecture, therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection, but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data, we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500, our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably, EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at this https URL.
Abstract (translated)
基于Transformer的模型在边缘检测方面取得了显著进步，但其高昂的计算成本是一个限制因素。最近的研究显示，视觉Mamba（一种轻量级网络）表现出色，在捕捉长距离依赖关系时非常高效。受此启发，我们提出了一种名为EDMB的新颖边缘探测器，它能够利用Mamba高效生成高质量、多粒度边缘。 在EDMB中，Mamba与全局-局部架构相结合，因此它可以同时关注整体信息和细粒度线索。细粒度线索对于边缘检测至关重要，但在普通Mamba中通常被忽略。我们设计了一种新的解码器来融合全局特征和细粒度特征以构建可学习的高斯分布，并通过从这些分布中采样生成多粒度边缘。 为了使多粒度边缘适用于单标签数据，我们引入了证据下界（Evidence Lower Bound）损失来监督分布的学习过程。在BSDS500这一多标签数据集上，我们的EDMB模型在未使用多尺度测试或额外PASCAL-VOC数据的情况下达到了具有竞争力的单一粒度ODS 0.837和多粒度ODS 0.851。 值得注意的是，EDMB可以扩展应用于NYUDv2和BIPED等单标签数据集。源代码可在以下链接获取：[提供URL]（请将实际链接替换为提供的示例中的占位符）。
URL
https://arxiv.org/abs/2501.04846
