Search Results for author:
Found 7 papers, 7 papers with code
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work.
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality).
Ranked #1 on
Semantic Segmentation
on SpectralWaste
Capturing Omni-Range Context for Omnidirectional Segmentation
Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving.
Ranked #10 on
Semantic Segmentation
on DensePASS
(using extra training data)
Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images
Semantic segmentation has made striking progress due to the success of deep convolutional neural networks.
Ranked #11 on
Semantic Segmentation
on EventScape
DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation through SwaftNet for Surrounding Sensing
Semantically interpreting the traffic scene is crucial for autonomous transportation and robotics systems.
Ranked #35 on
Semantic Segmentation
on DensePASS
ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
The main contributions lie in the Attention Complementary Module (ACM) and the architecture with three parallel branches.
Ranked #4 on
Semantic Segmentation
on KITTI-360
