We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
Additionally, we introduce a novel emoji representation based on their visual emotional response which supports a deeper understanding of the emoji modality and their usage on social media.
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals.
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses.
Compared to sub-pixel convolution initialized with schemes designed for standard convolution kernels, it is free from checkerboard artifacts immediately after initialization.
no code implementations • 22 May 2017 • Ozan Oktay, Enzo Ferrante, Konstantinos Kamnitsas, Mattias Heinrich, Wenjia Bai, Jose Caballero, Stuart Cook, Antonio de Marvao, Timothy Dawes, Declan O'Regan, Bernhard Kainz, Ben Glocker, Daniel Rueckert
However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge.
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Convolutional neural networks have enabled accurate image super-resolution in real-time.
Ranked #11 on Video Super-Resolution on MSU Video Upscalers: Quality Enhancement (VMAF metric)
We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models.
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.
This means that the super-resolution (SR) operation is performed in HR space.
Ranked #1 on Video Super-Resolution on Xiph HD - 4x upscaling
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Ranked #3 on Image Super-Resolution on VggFace2 - 8x upscaling