Search Results for author:
Found 16 papers, 10 papers with code
SiamMask: A Framework for Fast Online Object Tracking and Segmentation
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.
Attacking deep networks with surrogate-based adversarial black-box methods is easy
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
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.
Hierarchical interaction network for video object segmentation from referring expressions
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
Do Different Tracking Tasks Require Different Appearance Models?
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
Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks
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.
Anchor Diffusion for Unsupervised Video Object Segmentation
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
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation
Many applications require a camera to be relocalised online, without expensive offline training on the target scene.
Fast Online Object Tracking and Segmentation: A Unifying Approach
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
Meta-learning with differentiable closed-form solvers
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.
Long-term Tracking in the Wild: A Benchmark
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms.
End-to-end representation learning for Correlation Filter based tracking
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
Fully-Convolutional Siamese Networks for Object Tracking
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
Learning feed-forward one-shot learners
In this paper, we propose a method to learn the parameters of a deep model in one shot.
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #28 on
Visual Object Tracking
on TrackingNet
