21 citations · 22 across the 2 of their papers we have counts for
7 papers
Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks
Luca Bertinetto, Romain Mueller, Konstantinos Tertikas +2
Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than th…
Anchor Diffusion for Unsupervised Video Object Segmentation
Zhao Yang, Qiang Wang, Luca Bertinetto +3
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches te…
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation
Tommaso Cavallari, Luca Bertinetto, Jishnu Mukhoti +2
Many applications require a camera to be relocalised online, without expensive offline training on the target scene. Whilst both keyframe and sparse keypoint matching methods can b…
Fast Online Object Tracking and Segmentation: A Unifying Approach
Qiang Wang, Li Zhang, Luca Bertinetto +2
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbe…
Meta-learning with differentiable closed-form solvers
Luca Bertinetto, João F. Henriques, Philip H. S. Torr +1
Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot lear…
Long-term Tracking in the Wild: A Benchmark
Jack Valmadre, Luca Bertinetto, João F. Henriques +5
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining sta…