2 citations · 3 across the 2 of their papers we have counts for
5 papers
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Stephen Gould, Dylan Campbell, Itzik Ben-Shabat +2
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possib…
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet +1
Although 3D Convolutional Neural Networks are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and com…
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Although deep learning greatly improves the performance of semantic segmentation, its success mainly lies in object central areas without accurate edges. As superpixels are a popul…
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet +1
Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory…
Fast and Differentiable Message Passing on Pairwise Markov Random Fields
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-cra…