Search Results for author: Luca Bertinetto

Found 12 papers, 8 papers with code

Do Different Tracking Tasks Require Different Appearance Models?

1 code implementation5 Jul 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 used to obtain performance that is competitive against specialised methods for all the five tasks considered.

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

On Episodes, Prototypical Networks, and Few-shot Learning

1 code implementation17 Dec 2020 Steinar Laenen, Luca Bertinetto

Episodic learning is a popular practice among researchers and practitioners interested in few-shot learning.

Few-Shot Learning

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

4 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.

Few-Shot Learning

Fully-Convolutional Siamese Networks for Object Tracking

5 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 Tracking

Staple: Complementary Learners for Real-Time Tracking

2 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.

Visual Object Tracking

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