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The Visual Object Tracking VOT2016 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Expand
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SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
TLDR
Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region. Expand
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Transfer Learning Based Visual Tracking with Gaussian Processes Regression
TLDR
We propose a new transfer learning based visual tracker using Gaussian Processes Regression (GPR), and introduce a latent variable to assist the tracking decision. Expand
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The Visual Object Tracking VOT2017 Challenge Results
TLDR
The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Expand
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DCFNet: Discriminant Correlation Filters Network for Visual Tracking
TLDR
We present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously. Expand
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An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
TLDR
We propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. Expand
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Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks
TLDR
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Expand
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Pose-Driven Deep Convolutional Model for Person Re-identification
TLDR
We propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Expand
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View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
TLDR
We propose a view adaptive recurrent neural network with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Expand
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A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection
TLDR
In this paper, we present a deep regression architecture with two-stage re-initialization to explicitly deal with the initialization problem. Expand
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