Deep Pose Tracking with Partified Watermark Combinations


Deep Pose Tracking with Partified Watermark Combinations – Nam-style images of a child are a common feature of children. In this paper, we apply a method to segment images of children in both 2D and 3D scenarios using a deep convolutional network (CNN). We use the deep learning tool ChainView to perform classification on these images. We conduct experiments on over 500 children. The results show that our method outperforms methods such as the CNN, which uses 3D models and 3D hand-crafted features. Additionally, we report improvement and confidence levels of our classification results.

Automating the localization of human-based models is one of the most challenging tasks among machine learning algorithms. For this work, we propose a novel, deep CNN-based framework for semantic object localization. Our CNN architecture achieves state-of-the-art performance in the semantic object tracking and object-level segmentation scenarios using a single frame of video. Experiments show that our framework significantly outperforms both state-of-the-art and fully-convolutional CNN models for various tasks without the need for a hand-crafted semantic model or hand-tuning of the model. We also achieve a 20x improvement in object tracking speed compared to our proposed framework by incorporating a fully convolutional neural network.

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Deep Pose Tracking with Partified Watermark Combinations

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  • A Simple but Effective Framework For Textual Similarity

    Feature Learning for Image Search via Dynamic Contextual Policy SearchAutomating the localization of human-based models is one of the most challenging tasks among machine learning algorithms. For this work, we propose a novel, deep CNN-based framework for semantic object localization. Our CNN architecture achieves state-of-the-art performance in the semantic object tracking and object-level segmentation scenarios using a single frame of video. Experiments show that our framework significantly outperforms both state-of-the-art and fully-convolutional CNN models for various tasks without the need for a hand-crafted semantic model or hand-tuning of the model. We also achieve a 20x improvement in object tracking speed compared to our proposed framework by incorporating a fully convolutional neural network.


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