Visual Tracking by Joint Deep Learning with Pose Estimation


Visual Tracking by Joint Deep Learning with Pose Estimation – Deep learning-based neural networks have gained popularity recently due to their ability to produce accurate object recognition. This work addresses the problem of learning and training a deep network with pose estimation as a feature vector. In this paper, we show that existing deep neural network based pose estimation methods suffer from the same limitation as deep network based neural network models. Although pose representation is a fundamental issue for most pose estimation models, pose estimation and feature vector estimation are more useful in many applications. We present a novel framework, named Pose-Deep network (PDSNet) with a simple model architecture to build a neural network model for pose estimation. PDSNet offers the state-of-the-art performance on most state-of-the-art face verification datasets, surpassing previous state-of-the-art approaches.

In this note, we describe a simple implementation of the popular DeepPPA – a Multi-Parallel AdaBoost Library. On the one hand, this library has been developed with the specific goal of building a powerful algorithm to solve difficult multi-task tasks. On the other hand, we also provide a simple algorithm which we have been using recently in PASCAL VOC.

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Visual Tracking by Joint Deep Learning with Pose Estimation

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  • An Adaptive Aggregated Convex Approximation for Log-Linear Models

    DeepPPA: A Multi-Parallel AdaBoost Library for Deep LearningIn this note, we describe a simple implementation of the popular DeepPPA – a Multi-Parallel AdaBoost Library. On the one hand, this library has been developed with the specific goal of building a powerful algorithm to solve difficult multi-task tasks. On the other hand, we also provide a simple algorithm which we have been using recently in PASCAL VOC.


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