Search Results for author: Luca Bertinetto

Found 16 papers, 10 papers with code

SiamMask: A Framework for Fast Online Object Tracking and Segmentation

no code implementations5 Jul 2022 Weiming Hu, Qiang Wang, Li Zhang, Luca Bertinetto, Philip H. S. Torr

In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method.

Multiple Object Tracking Semantic Segmentation +3

Attacking deep networks with surrogate-based adversarial black-box methods is easy

1 code implementation ICLR 2022 Nicholas A. Lord, Romain Mueller, Luca Bertinetto

A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search.

Parameter-free Online Test-time Adaptation

1 code implementation CVPR 2022 Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, Luca Bertinetto

An interesting and practical paradigm is online test-time adaptation, according to which training data is inaccessible, no labelled data from the test distribution is available, and adaptation can only happen at test time and on a handful of samples.

Do Different Tracking Tasks Require Different Appearance Models?

1 code implementation NeurIPS 2021 Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto

We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.

Multi-Object Tracking Multi-Object Tracking and Segmentation +10

Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks

1 code implementation CVPR 2020 Luca Bertinetto, Romain Mueller, Konstantinos Tertikas, Sina Samangooei, Nicholas A. Lord

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 the ground truth as equally wrong.

Image Classification

Fast Online Object Tracking and Segmentation: A Unifying Approach

3 code implementations CVPR 2019 Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H. S. Torr

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.

Real-Time Visual Tracking Semi-Supervised Semantic Segmentation +2

Meta-learning with differentiable closed-form solvers

5 code implementations ICLR 2019 Luca Bertinetto, João F. Henriques, Philip H. S. Torr, Andrea Vedaldi

The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data.

BIG-bench Machine Learning Few-Shot Learning +1

Fully-Convolutional Siamese Networks for Object Tracking

8 code implementations30 Jun 2016 Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.

object-detection Object Detection +1

Staple: Complementary Learners for Real-Time Tracking

3 code implementations CVPR 2016 Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.

regression Visual Object Tracking

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