Search Results for author:
Found 3 papers, 0 papers with code
Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation
It parses the entire image as a fine-to-coarse hierarchical structure of constituent visual patterns, and captures multiscale features by progressively merging adjacent superpixels as graph nodes.
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
For the Depth branch, we propose a Pixel Difference Convolution (PDC) to consider local and detailed geometric information in Depth data via aggregating both intensity and gradient information.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
DCANet: Differential Convolution Attention Network for RGB-D Semantic Segmentation
A two-branch network built with DCA and EDCA, called Differential Convolutional Network (DCANet), is proposed to fuse local and global information of two-modal data.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
