10 citations · 19 across the 5 of their papers we have counts for
6 papers
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition
Umar Asif, Deval Mehta, Stefan von Cavallar +2
Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics…
SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
Umar Asif, Stefan Von Cavallar, Jianbin Tang +1
Falling can have fatal consequences for elderly people especially if the fallen person is unable to call for help due to loss of consciousness or any injury. Automatic fall detecti…
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Umar Asif, Jianbin Tang, Stefan Harrer
Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory r…
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Umar Asif, Subhrajit Roy, Jianbin Tang +1
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is cha…
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark
Subhrajit Roy, Umar Asif, Jianbin Tang +1
Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of d…
Densely Supervised Grasp Detector (DSGD)
Umar Asif, Jianbin Tang, Stefan Harrer
This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning framework which combines CNN structures with layer-wise feature fusion and produces grasps and their c…