4 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
VideoSEMA: a scalable and efficient Mamba-like attention for video understanding
Nhat Thanh Tran, Fanghui Xue, Shuai Zhang +4
We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space…
SEMA: a Scalable and Efficient Mamba like Attention via Token Localization and Averaging
Nhat Thanh Tran, Fanghui Xue, Shuai Zhang +4
Attention is the critical component of a transformer. Yet the quadratic computational complexity of vanilla full attention in the input size and the inability of its linear attenti…
AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs
Yunling Zheng, Zeyi Xu, Fanghui Xue +5
We propose and demonstrate an alternating Fourier and image domain filtering approach for feature extraction as an efficient alternative to build a vision backbone without using th…
Improving Network Slimming with Nonconvex Regularization
Kevin Bui, Fredrick Park, Shuai Zhang +2
Convolutional neural networks (CNNs) have developed to become powerful models for various computer vision tasks ranging from object detection to semantic segmentation. However, mos…
Regularized Structured Sparsity Convolutional Neural Networks
Kevin Bui, Fredrick Park, Shuai Zhang +2
Deepening and widening convolutional neural networks (CNNs) significantly increases the number of trainable weight parameters by adding more convolutional layers and feature maps p…
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
Penghang Yin, Shuai Zhang, Jiancheng Lyu +3
We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights. The set constraint that characterizes the quantization of weights is…