Search Results for author:
Found 7 papers, 3 papers with code
Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
To extend our approach to video, we integrate ConvNets with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks.
Ranked #75 on
Action Recognition
on UCF101
Action Recognition for American Sign Language
In this research, we present our findings to recognize American Sign Language from series of hand gestures.
PSO-Convolutional Neural Networks with Heterogeneous Learning Rate
In such framework, the vector of weights of each ConvNet is typically cast as the position of a particle in phase space whereby PSO collaborative dynamics intertwines with Stochastic Gradient Descent (SGD) in order to boost training performance and generalization.
Ranked #39 on
Image Classification
on CIFAR-10
An Improvement for Capsule Networks using Depthwise Separable Convolution
Capsule Networks face a critical problem in computer vision in the sense that the image background can challenge its performance, although they learn very well on training data.
Rethinking Recurrent Neural Networks and Other Improvements for Image Classification
In addition, we extend the training strategy so that our model performs comparably to leading models and can even match the state-of-the-art models on several challenging datasets (e. g., SVHN (0. 99), Cifar-100 (0. 9027) and Cifar-10 (0. 9852)).
Ranked #1 on
Image Classification
on Surrey ASL
Offline and Online Deep Learning for Image Recognition
Image recognition using Deep Learning has been evolved for decades though advances in the field through different settings is still a challenge.
Advanced Capsule Networks via Context Awareness
Capsule Networks (CN) offer new architectures for Deep Learning (DL) community.
