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Fully-Convolutional Siamese Networks for Object Tracking
TLDR
A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks. Expand
Struck: Structured output tracking with kernels
TLDR
This paper presents a framework for adaptive visual object tracking based on structured output prediction that is able to avoid the need for an intermediate classification step, and uses a kernelized structured output support vector machine (SVM), which is learned online to provide adaptive tracking. Expand
Staple: Complementary Learners for Real-Time Tracking
TLDR
It is shown that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks. Expand
Learning to Compare: Relation Network for Few-Shot Learning
TLDR
A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning. Expand
Conditional Random Fields as Recurrent Neural Networks
TLDR
A new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced, and top results are obtained on the challenging Pascal VOC 2012 segmentation benchmark. Expand
End-to-End Representation Learning for Correlation Filter Based Tracking
TLDR
This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the Cor correlation filter. Expand
Struck: Structured Output Tracking with Kernels
TLDR
A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance. Expand
The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment. Expand
Deeply Supervised Salient Object Detection with Short Connections
TLDR
A new saliency method is proposed by introducing short connections to the skip-layer structures within the HED architecture, which produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency, effectiveness, and simplicity over the existing algorithms. Expand
An embarrassingly simple approach to zero-shot learning
TLDR
This paper describes a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. Expand
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