no code implementations • 5 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.
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.
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.
no code implementations • British Machine Vision Conference 2021 • Zhao Yang, Yansong Tang, Luca Bertinetto, Hengshuang Zhao, Philip Torr
In this paper, we investigate the problem of video object segmentation from referring expressions (VOSRE).
Ranked #1 on Referring Expression Segmentation on J-HMDB (Precision@0.9 metric)
Optical Flow Estimation Referring Expression Segmentation +3
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.
Ranked #2 on Video Object Segmentation on DAVIS 2017 (mIoU metric)
Multi-Object Tracking Multi-Object Tracking and Segmentation +10
1 code implementation • NeurIPS 2021 • Steinar Laenen, Luca Bertinetto
But is this always necessary?
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.
1 code implementation • ICCV 2019 • Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, Philip H. S. Torr
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow.
Ranked #19 on Unsupervised Video Object Segmentation on DAVIS 2016 val
no code implementations • 20 Jun 2019 • Tommaso Cavallari, Luca Bertinetto, Jishnu Mukhoti, Philip Torr, Stuart Golodetz
Many applications require a camera to be relocalised online, without expensive offline training on the target scene.
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.
Ranked #3 on Visual Object Tracking on YouTube-VOS 2018
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.
no code implementations • ECCV 2018 • Jack Valmadre, Luca Bertinetto, João F. Henriques, Ran Tao, Andrea Vedaldi, Arnold Smeulders, Philip Torr, Efstratios Gavves
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms.
no code implementations • CVPR 2017 • Jack Valmadre, Luca Bertinetto, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations.
Ranked #3 on Visual Object Tracking on OTB-50
10 code implementations • 30 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.
Ranked #3 on Visual Object Tracking on OTB-50
no code implementations • NeurIPS 2016 • Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi
In this paper, we propose a method to learn the parameters of a deep model in one shot.
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.
Ranked #29 on Visual Object Tracking on TrackingNet