边界注意力：学习曲线、角点、连接点与分组
Boundary Attention: Learning curves, corners, junctions and grouping
AI总结：
该研究提出一种带边界注意力机制的轻量级自底向上网络，可学习局部边界结构与尺度一致性，生成丰富的非栅格化边界表示，能在合成形状训练后泛化至低光照噪声图像的边界提取任务。
摘要：
我们提出了一种轻量级网络，可推断分组与边界，包括曲线、角点和连接点。该网络以自底向上的方式运行，类似于亚像素边缘定位与边缘连接的经典方法，但采用了更高维度的局部边界结构表示，且局部尺度与空间一致性的概念是通过学习而非人工设计得到的。我们的网络使用了一种名为边界注意力（boundary attention）的机制：这是一种具备几何感知的局部注意力操作，当被密集且重复地应用时，会逐步细化像素分辨率的变量场，这些变量可指定图像内每个重叠 patch 中的边界结构。与许多生成栅格化二值边缘图的边缘检测器不同，我们的模型为每个局部区域的几何结构提供了丰富的非栅格化表示。我们发现，其有意引入的几何偏置使其能够在简单合成形状上训练，随后可泛化到从有噪声的低光照照片中提取边界的任务。
英文摘要：
We present a lightweight network that infers grouping and boundaries, including curves, corners and junctions. It operates in a bottom-up fashion, analogous to classical methods for sub-pixel edge localization and edge-linking, but with a higher-dimensional representation of local boundary structure, and notions of local scale and spatial consistency that are learned instead of designed. Our network uses a mechanism that we call boundary attention: a geometry-aware local attention operation that, when applied densely and repeatedly, progressively refines a pixel-resolution field of variables that specify the boundary structure in every overlapping patch within an image. Unlike many edge detectors that produce rasterized binary edge maps, our model provides a rich, unrasterized representation of the geometric structure in every local region. We find that its intentional geometric bias allows it to be trained on simple synthetic shapes and then generalize to extracting boundaries from noisy low-light photographs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
