4 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
TinyDrop: Tiny Model Guided Token Dropping for Vision Transformers
Guoxin Wang, Qingyuan Wang, Binhua Huang +2
Vision Transformers (ViTs) achieve strong performance in image classification but incur high computational costs from processing all image tokens. To reduce inference costs in larg…
MoCrop: Training Free Motion Guided Cropping for Efficient Video Action Recognition
Binhua Huang, Wendong Yao, Shaowu Chen +3
Standard video action recognition models often process typically resized full frames, suffering from spatial redundancy and high computational costs. To address this, we introduce…
Optimal Brain Connection: Towards Efficient Structural Pruning
Shaowu Chen, Wei Ma, Binhua Huang +5
Structural pruning has been widely studied for its effectiveness in compressing neural networks. However, existing methods often neglect the interconnections among parameters. To a…
ORXE: Orchestrating Experts for Dynamically Configurable Efficiency
Qingyuan Wang, Guoxin Wang, Barry Cardiff +1
This paper presents ORXE, a modular and adaptable framework for achieving real-time configurable efficiency in AI models. By leveraging a collection of pre-trained experts with div…
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
Guoxin Wang, Qingyuan Wang, Ganesh Neelakanta Iyer +2
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and…