Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning


Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning – We investigate methods for unsupervised learning of video-based motion segmentation from images. We exploit the fact that video frames have varying spatial resolution for segmentation and pose. Additionally, frame-level object identification from 2D depth images is a key challenge in videos. In this research we propose a novel unsupervised learning architecture, which has the ability to learn an object-level pose from 2D depth images without the need for a deep neural network. Specifically, our model trains a convolutional neural network to learn a pose representation based on 2D depth images and then learn a pose from a convolutional neural network. We demonstrate that our proposed model, named ImageNet, significantly improves object segmentation with end-to-end training. We study our method on four real-world video datasets, using videos of humans interacting with objects and interacting in different ways.

We present a recurrent neural network framework to support a variety of recurrent neural networks. The framework is designed to learn recurrent neural networks based on the constraints of the semantic embedding domain based on attention mechanisms. We leverage the constraints to extract contextual dependencies and solve a joint optimization problem with support vector machines. We then provide the support vector machine to perform the learning. We demonstrate the proposed framework in a benchmark performance-based algorithm.

In this paper, we propose a novel method for learning from video. The proposed learning method is a recurrent neural network model trained end-to-end on the temporal representations of the input video frames. Our neural network model learns to discriminate the frames using a convolutional neural network which is trained on the input videos. Experiments show that our method can lead to a higher performance than the previous state-of-the-art models by achieving the best performance.

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Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning

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  • Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection

    Improving Recurrent Neural Network with Contextual DependenceWe present a recurrent neural network framework to support a variety of recurrent neural networks. The framework is designed to learn recurrent neural networks based on the constraints of the semantic embedding domain based on attention mechanisms. We leverage the constraints to extract contextual dependencies and solve a joint optimization problem with support vector machines. We then provide the support vector machine to perform the learning. We demonstrate the proposed framework in a benchmark performance-based algorithm.

    In this paper, we propose a novel method for learning from video. The proposed learning method is a recurrent neural network model trained end-to-end on the temporal representations of the input video frames. Our neural network model learns to discriminate the frames using a convolutional neural network which is trained on the input videos. Experiments show that our method can lead to a higher performance than the previous state-of-the-art models by achieving the best performance.


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