Recurrent Neural Models for Autonomous Driving


Recurrent Neural Models for Autonomous Driving – This paper addresses the problem of learning object based features from the semantic representations of an object. We present a novel representation learning approach for deep recurrent networks, which learns to represent objects as vectors. This approach relies on a deep recurrent network or a dictionary trained only on vector representations. We study a novel approach combining recurrent features from both neural representations and a dictionary trained on neural representations. We demonstrate the effectiveness of our method with the help of a novel model representation training algorithm and extensive experiments on both synthetic and real-world datasets.

This paper presents a novel approach for video tracking in a visual sense for a new application: video tracking in an unsupervised environment. Our vision system relies on a novel form of multi-class clustering within which each cluster class represents a particular feature, which is then selected by a set of local features. Since the user-defined cluster has a more specific shape and appearance, it is easier to find the best clustering scheme. To make the system aware of the clustering scheme to make the best use of the available data, we first obtain a graph of the clustering scheme by using both the visual aspect, the structural components of the cluster and multi-class information that are used to classify the video sequences and the spatial information needed to infer a given feature. We show that when using a local feature representation, the user’s visual knowledge can be used to generate a better clustering scheme. Finally, we propose a novel clustering technique called Multiple-Class clustering which consists in clustering into multiple class groups. The approach to this new multi-class clustering scheme is presented in a detailed study.

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Recurrent Neural Models for Autonomous Driving

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    Learning LSTM from Unlearnable VideosThis paper presents a novel approach for video tracking in a visual sense for a new application: video tracking in an unsupervised environment. Our vision system relies on a novel form of multi-class clustering within which each cluster class represents a particular feature, which is then selected by a set of local features. Since the user-defined cluster has a more specific shape and appearance, it is easier to find the best clustering scheme. To make the system aware of the clustering scheme to make the best use of the available data, we first obtain a graph of the clustering scheme by using both the visual aspect, the structural components of the cluster and multi-class information that are used to classify the video sequences and the spatial information needed to infer a given feature. We show that when using a local feature representation, the user’s visual knowledge can be used to generate a better clustering scheme. Finally, we propose a novel clustering technique called Multiple-Class clustering which consists in clustering into multiple class groups. The approach to this new multi-class clustering scheme is presented in a detailed study.


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