2 citations · 5 across the 5 of their papers we have counts for
5 papers
Fusion of regional and sparse attention in Vision Transformers
Nabil Ibtehaz, Ning Yan, Masood Mortazavi +1
Modern vision transformers leverage visually inspired local interaction between pixels through attention computed within window or grid regions, in contrast to the global attention…
Modally Reduced Representation Learning of Multi-Lead ECG Signals through Simultaneous Alignment and Reconstruction
Nabil Ibtehaz, Masood Mortazavi
Electrocardiogram (ECG) signals, profiling the electrical activities of the heart, are used for a plethora of diagnostic applications. However, ECG systems require multiple leads o…
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers
Nabil Ibtehaz, Ning Yan, Masood Mortazavi +1
Transformers have elevated to the state-of-the-art vision architectures through innovations in attention mechanism inspired from visual perception. At present two classes of attent…
ACC-UNet: A Completely Convolutional UNet model for the 2020s
Nabil Ibtehaz, Daisuke Kihara
This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision. A similar trend is followed in medical imaging, UNet, one of the…
Application of Sequence Embedding in Protein Sequence-Based Predictions
Nabil Ibtehaz, Daisuke Kihara
In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-speci…