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Found 19 papers, 7 papers with code
Two-Person Interaction Augmentation with Skeleton Priors
Close and continuous interaction with rich contacts is a crucial aspect of human activities (e. g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc.
Pose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor.
INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries.
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.
CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy
Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment.
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.
A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist.
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.
Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video
To this end, we propose to classify Parkinson's tremor since it is one of the most predominant symptoms of PD with strong generalizability.
A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
Our network exploits the spatial connections between human keypoints and object keypoints to capture their fine-grained structural interactions via graph convolutions.
Ranked #22 on
Human-Object Interaction Detection
on V-COCO
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity
Nowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems.
Predicting Sleeping Quality using Convolutional Neural Networks
Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders.
Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
Our prediction performance achieves state-of-the-art research on both datasets.
Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework
Providing early diagnosis of cerebral palsy (CP) is key to enhancing the developmental outcomes for those affected.
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.
Illumination-Based Data Augmentation for Robust Background Subtraction
A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames.
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling
In this paper, we propose a new deep network to tackle these challenges by creating a natural motion manifold that is versatile for many applications.
Illumination-Aware Multi-Task GANs for Foreground Segmentation
Foreground-background segmentation has been an active research area over the years.
Ranked #1 on
Video Background Subtraction
on SABS
