10 citations · 10 across the 2 of their papers we have counts for
5 papers
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
Chaitanya Kaul, Nick Pears, Suresh Manandhar
The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we prop…
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
Chaitanya Kaul, Nick Pears, Hang Dai +2
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms wi…
Penalizing small errors using an Adaptive Logarithmic Loss
Chaitanya Kaul, Nick Pears, Hang Dai +2
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth. Fundamentally, they define a functional landscape for travers…
SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing
Chaitanya Kaul, Nick Pears, Suresh Manandhar
Deep neural networks have established themselves as the state-of-the-art methodology in almost all computer vision tasks to date. But their application to processing data lying on…
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
Chaitanya Kaul, Suresh Manandhar, Nick Pears
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention archi…