A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
Deep multimodal learning: A survey on recent advances and trends
The success of deep learning has been a catalyst to solving increasingly complex machine-
learning problems, which often involve multiple data modalities. We review recent advances …
learning problems, which often involve multiple data modalities. We review recent advances …
A survey of model compression and acceleration for deep neural networks
Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …
recognition tasks. However, existing deep neural network models are computationally …
A low power, fully event-based gesture recognition system
We present the first gesture recognition system implemented end-to-end on event-based
hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real …
hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real …
Model compression and acceleration for deep neural networks: The principles, progress, and challenges
In recent years, deep neural networks (DNNs) have received increased attention, have been
applied to different applications, and achieved dramatic accuracy improvements in many …
applied to different applications, and achieved dramatic accuracy improvements in many …
End-to-end multimodal emotion recognition using deep neural networks
Automatic affect recognition is a challenging task due to the various modalities emotions can
be expressed with. Applications can be found in many domains including multimedia …
be expressed with. Applications can be found in many domains including multimedia …
A deep neural framework for continuous sign language recognition by iterative training
R Cui, H Liu, C Zhang - IEEE Transactions on Multimedia, 2019 - ieeexplore.ieee.org
This work develops a continuous sign language (SL) recognition framework with deep
neural networks, which directly transcribes videos of SL sentences to sequences of ordered …
neural networks, which directly transcribes videos of SL sentences to sequences of ordered …
RGB-D-based human motion recognition with deep learning: A survey
Human motion recognition is one of the most important branches of human-centered
research activities. In recent years, motion recognition based on RGB-D data has attracted …
research activities. In recent years, motion recognition based on RGB-D data has attracted …
A comprehensive study on deep learning-based methods for sign language recognition
In this paper, a comparative experimental assessment of computer vision-based methods for
sign language recognition is conducted. By implementing the most recent deep neural …
sign language recognition is conducted. By implementing the most recent deep neural …
Recurrent convolutional neural networks for continuous sign language recognition by staged optimization
R Cui, H Liu, C Zhang - … of the IEEE conference on computer …, 2017 - openaccess.thecvf.com
This work presents a weakly supervised framework with deep neural networks for vision-
based continuous sign language recognition, where the ordered gloss labels but no exact …
based continuous sign language recognition, where the ordered gloss labels but no exact …