Deep Learning with a Unified Deep Convolutional Network for Video Classification

Deep Learning with a Unified Deep Convolutional Network for Video Classification – In this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning object detection and trajectory based object classification, without using any hand-crafted convolutional features. Compared to existing CNN models with a very small number of parameters, our CNN has a few parameters which are more discriminative to improve object detection. We show that our CNN is not only able to reliably classify high quality object instances without any hand-crafted object features. This is important because CNN can be used for improving object category accuracy if the 2D object recognition process is used. In addition to CNN, our CNN is also able to accurately classify objects which are very dense objects. Our CNN is implemented using an interactive 3D object prediction platform which demonstrates our accuracy on the challenging task of 2D objects classification on a 3D MNIST dataset.

This paper presents a novel, flexible and efficient method for learning high-dimensional semantic embedding functions in a high-dimensional, yet unsupervised, spatial context with a high-dimensional image. The method provides a new perspective on the representation and learning of semantic structures, which is applicable to a variety of semantic representations. To obtain this perspective, the use of semantic features and prior knowledge is augmented with an external framework. To the best of our knowledge, this is the first experimental investigation of this approach in any well-studied semantic representation task. Extensive experiments on several real-world datasets are conducted to demonstrate the effectiveness of our approach; we show significant performance improvements over our previous method.

Learning Dynamic Text Embedding Models Using CNNs

Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

Deep Learning with a Unified Deep Convolutional Network for Video Classification

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  • Interpretable Machine Learning: A New Concept for Theory and Application to Derivative-Free MLPs

    Direction of ScaleThis paper presents a novel, flexible and efficient method for learning high-dimensional semantic embedding functions in a high-dimensional, yet unsupervised, spatial context with a high-dimensional image. The method provides a new perspective on the representation and learning of semantic structures, which is applicable to a variety of semantic representations. To obtain this perspective, the use of semantic features and prior knowledge is augmented with an external framework. To the best of our knowledge, this is the first experimental investigation of this approach in any well-studied semantic representation task. Extensive experiments on several real-world datasets are conducted to demonstrate the effectiveness of our approach; we show significant performance improvements over our previous method.


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