151 citations · 561 across the 38 of their papers we have counts for
16 papers · 1 filter
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
Jie Hu, Li Shen, Samuel Albanie +2
While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from captu…
Learning to Read by Spelling: Towards Unsupervised Text Recognition
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
This work presents a method for visual text recognition without using any paired supervisory data. We formulate the text recognition task as one of aligning the conditional distrib…
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
Samuel Albanie, Arsha Nagrani, Andrea Vedaldi +1
Obtaining large, human labelled speech datasets to train models for emotion recognition is a notoriously challenging task, hindered by annotation cost and label ambiguity. In this…
Semi-convolutional Operators for Instance Segmentation
David Novotny, Samuel Albanie, Diane Larlus +1
Object detection and instance segmentation are dominated by region-based methods such as Mask RCNN. However, there is a growing interest in reducing these problems to pixel labelin…
Inductive Visual Localisation: Factorised Training for Superior Generalisation
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine transla…
Large scale evaluation of local image feature detectors on homography datasets
Karel Lenc, Andrea Vedaldi
We present a large scale benchmark for the evaluation of local feature detectors. Our key innovation is the introduction of a new evaluation protocol which extends and improves the…