论文标题
SAPA：相似性吸引点归属于功能上的采样
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
论文作者
论文摘要
我们将点隶属关系引入特征UPS采样，该概念描述了每个上采样点的隶属关系，该点与具有语义相似性的本地解码器特征点形成的语义群集。通过重新思考点的隶属关系，我们提出了一种通用公式，用于产生上采样内核。内核不仅鼓励语义平滑度，而且还鼓励上采样的特征图中的边界清晰度。此类属性对于某些密集的预测任务（例如语义分割）特别有用。我们配方的关键思想是通过比较每个编码器特征点与解码器特征的空间相关局部区域之间的相似性来生成相似性感知的内核。通过这种方式，编码器特征点可以作为提示，以告知UPSPEMPLED特征点的语义集群。为了体现该配方，我们进一步实例化了轻巧的增加采样操作员，称为相似性 - 吸引点隶属关系（SAPA），并研究其变体。 SAPA会邀请许多密集的预测任务（包括语义分割，对象检测，深度估计和图像垫）进行一致的性能改进。代码可用：https：//github.com/poppinace/sapa
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapa
