FGCNet has three new modules connected in sequence: (i) a local graph convolution block takes point-wise features as inputs and encodes local contextual infor- mation to extract local features; (ii) a fast graph message-passing network takes local features as inputs, encodes two-view global con- textual information, to ...
This paper proposes a fast graph convolution network (FGCNet) to match two sets of sparse features. FGCNet has three new modules connected in sequence.
In this paper, we propose a Feature Interaction Convolutional Network (FICN) for knowledge graph embedding, which uses three methods: Random Permutation, ...
Co-authors ; FGCNet: Fast Graph Convolution for Matching Features. L Liu, L Pan, W Luo, Q Xu, Y Wen, J Li. ISMAR, 2022. 4, 2022 ; Language-driven All-in-one ...
Jan 6, 2023 · Abstract. This technical report introduces CyberLoc, an image-based visual localization pipeline for robust and accurate long-term pose es-.
Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks · Computer Science, ...
This paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two modules.
Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Information Fusion, 2020 ...
7 days ago · Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference.
Missing: FGCNet: Matching
Feb 3, 2024 · For instance, Wang et al. 30 developed FGCNet, a deep feature fusion (DFF) model that combines multiple deep feature representations from ...