WebMar 12, 2024 · A heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral-spatial features at pixel and superpixel levels, respectively. 46 View 1 excerpt WebQ. Liu, L. Xiao, J. Yang and Z. Wei, "CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification," in …
图卷积网络GCN---底层逻辑最简单直白的理解
WebAmong those applications, intelligent transportation system (ITS) and autonomous vehicles are anticipated to bring new experiences with enhanced efficiency and safety to road … WebA heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral-spatial features at pixel and superpixel levels, respectively. 46 View 1 excerpt red opium dc
CNN_Enhanced_GCN/CEGCN.py at master - Github
WebIn Ref. [59], a neural network named CNN-enhanced GCN (CEGCN) was designed using the properties of CNN to extract regular image regions and GCN to extract irregular … Web53]. Specifically, [28] propose a AS-GCN to dig the la-tent joint connectionsto boostthe recognition performance. A two-stream approach is presented in [46] and further ex-tended to four streams in [47]. [7] develops a decoupling GCN to increase the model capacity with no extra compu-tational cost. ResGCN is proposed in [53] which adopts WebTo fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral-spatial features at pixel and superpixel levels, … richer sounds dublin