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Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
Hyeonseob Nam,Bohyung Han +1 more
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Zhang et al. as discussed by the authors proposed a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN), which pretrain a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.Abstract:
We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation. Our network is composed of shared layers and multiple branches of domain-specific layers, where domains correspond to individual training sequences and each branch is responsible for binary classification to identify the target in each domain. We train the network with respect to each domain iteratively to obtain generic target representations in the shared layers. When tracking a target in a new sequence, we construct a new network by combining the shared layers in the pretrained CNN with a new binary classification layer, which is updated online. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state. The proposed algorithm illustrates outstanding performance compared with state-of-the-art methods in existing tracking benchmarks.read more
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Proceedings ArticleDOI
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
TL;DR: CutMix as discussed by the authors augments the training data by cutting and pasting patches among training images, where the ground truth labels are also mixed proportionally to the area of the patches.
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High Performance Visual Tracking with Siamese Region Proposal Network
TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
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Learning without Forgetting
Zhizhong Li,Derek Hoiem +1 more
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Fully-Convolutional Siamese Networks for Object Tracking
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End-to-End Representation Learning for Correlation Filter Based Tracking
TL;DR: In this paper, the Correlation Filter learner is interpreted as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the correlation filter.
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