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Found 7 papers, 2 papers with code
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction
SENet aims to preserve the structural consistency of the character and normalize complex noise.
Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry
Supervised learning depth estimation methods can achieve good performance when trained on high-quality ground-truth, like LiDAR data.
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network
However, in most existing Graph Convolutional Networks, the local attention mask is defined based on natural connections of human skeleton joints and ignores the dynamic relations for example between head, hands and feet joints.
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information
Our approach achieves the bestperformance on precision and accuracy of human fall event detection, compared with other existing dictionary learning methods.
Product Image Recognition with Guidance Learning and Noisy Supervision
Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers.
