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Found 8 papers, 4 papers with code
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition
Learning view-invariant representation is a key to improving feature discrimination power for skeleton-based action recognition.
A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction
As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data.
Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning
To understand the causal relationship between gaze movement and probe motion, our model exploits multitask learning to jointly learn two related tasks: predicting gaze movements and probe signals that an experienced sonographer would perform in routine obstetric scanning.
Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi-Hot Class Embedding
In this paper, we propose a novel way to create realistic human reactive motions which are not presented in the given dataset by mixing and matching different types of close interactions.
Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN).
Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction
This is because they ignore the impact of the implicit correlations between different types of road users on the trajectory to be predicted - for example, a nearby pedestrian has a different level of influence from a nearby car.
Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention
To highlight the capacity of the deep network in modelling input features, we utilize raw joint positions instead of hand-crafted features.
