On Point Affiliation in Feature Upsampling
Wenze Liu Hao Lu Yuliang Liu Zhiguo Cao
We introduce the notion of point affiliation into feature upsampling. By
abstracting a feature map into non-overlapped semantic clusters formed by
points of identical semantic meaning, feature upsampling can be viewed as point
affiliation -- designating a semantic cluster for each upsampled point. In the
framework of kernel-based dynamic upsampling, we show that an upsampled point
can resort to its low-res decoder neighbors and high-res encoder point to
reason the affiliation, conditioned on the mutual similarity between them. We
therefore present a generic formulation for generating similarity-aware
upsampling kernels and prove that such kernels encourage not only semantic
smoothness but also boundary sharpness. This formulation constitutes a novel,
lightweight, and universal upsampling solution, Similarity-Aware Point
Affiliation (SAPA). We show its working mechanism via our preliminary designs
with window-shape kernel. After probing the limitations of the designs on
object detection, we reveal additional insights for upsampling, leading to SAPA
with the dynamic kernel shape. Extensive experiments demonstrate that SAPA
outperforms prior upsamplers and invites consistent performance improvements on
a number of dense prediction tasks, including semantic segmentation, object
detection, instance segmentation, panoptic segmentation, image matting, and
depth estimation. Code is made available at: https://github.com/tiny-smart/sapa