**ShapeConv**, or **Shape-aware Convolutional layer**, is a convolutional layer for processing the depth feature in indoor RGB-D semantic segmentation. The depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a [convolution](https://paperswithcode.com/method/convolution) is applied on the re-weighted combination of these two components.

**PSFR-GAN** is a semantic-aware style transformation framework for face restoration. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of inputs.

PSFR-GAN

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

ShapeConv

ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation

using transfer learning to transfer knowledge from one building to predict the energy consumption of another building with scarce data

Source	ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com