10 citations · 25 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Towards a complete 3D morphable model of the human head
Stylianos Ploumpis, Evangelos Ververas, Eimear O' Sullivan +6
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of…
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…
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Stylianos Ploumpis, Haoyang Wang, Nick Pears +2
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class. In this context, we identify an interesting question…
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…