Search Results for author: Jianbin Tang

Found 10 papers, 4 papers with code

Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices

no code implementations24 Aug 2023 Hannah Zhou, Allison Chen, Celine Buer, Emily Chen, Kayleen Tang, Lauryn Gong, Zhiqi Liu, Jianbin Tang

This paper presents a cost-effective, low-power approach to unintentional fall detection using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly improve accuracy.

Knowledge Distillation Time Series

DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition

no code implementations21 Sep 2020 Umar Asif, Deval Mehta, Stefan von Cavallar, Jianbin Tang, Stefan Harrer

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 of the environments.

Action Recognition

SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience

no code implementations2 Apr 2020 Umar Asif, Stefan von Cavallar, Jianbin Tang, Stefan Harrer

First, we present a human pose based fall representation which is invariant to appearance characteristics.

3D Pose Estimation

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

2 code implementations17 Sep 2019 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 requirements.

Ensemble Learning General Classification +2

PubLayNet: largest dataset ever for document layout analysis

6 code implementations16 Aug 2019 Xu Zhong, Jianbin Tang, Antonio Jimeno Yepes

Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images.

Document Layout Analysis Transfer Learning

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

3 code implementations8 Mar 2019 Umar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.

Classification EEG +6

Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark

1 code implementation4 Feb 2019 Subhrajit Roy, Umar Asif, Jianbin Tang, Stefan Harrer

On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.

BIG-bench Machine Learning Classification +5

Densely Supervised Grasp Detector (DSGD)

no code implementations1 Oct 2018 Umar Asif, Jianbin Tang, Stefan Harrer

At the pixel-level, DSGD uses a fully convolutional network and predicts a grasp and its confidence at every pixel.

Region Proposal Robotic Grasping

Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips

no code implementations19 May 2017 Antonio Jimeno Yepes, Jianbin Tang, Benjamin Scott Mashford

We achieve this by training directly a binary hardware crossbar that accommodates the TrueNorth axon configuration constrains and we propose a different neuron model.

EEG Electroencephalogram (EEG) +1

Improving energy efficiency and classification accuracy of neuromorphic chips by learning binary synaptic crossbars

no code implementations25 May 2016 Antonio Jimeno Yepes, Jianbin Tang

Previous work has achieved this by training a network to learn continuous probabilities and deployment to a neuromorphic architecture by random sampling these probabilities.

General Classification

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