Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part Frames


Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part Frames – In this paper, we present a novel framework for learning 3D models in deep neural network. The proposed framework is based on a deep hierarchical model which consists of hierarchical components and a global topology representation. A deep hierarchical model is designed to learn the model parameters in a deep hierarchy. Then, the model parameters are learned using an embedding procedure. The embedding procedure can be used to dynamically embed parts of the model parameters into the global topology representation. In order to learn the model parameters, the global topology representation and their embedding are jointly learned in a fully supervised manner. We also propose a simple method to learn the model parameters, which utilizes the embedding procedure to learn the model parameters directly from the global topology representation. The proposed deep hierarchical model is shown to learn 3D model parameters efficiently by a real-world problem.

This paper presents an evolutionary algorithm for automatic object manipulation, namely, an algorithm for determining when a single object is manipulated effectively based on the observed context and on the object’s overall behavior. The proposed approach is based on the hypothesis that a single object is manipulated effectively by multiple objects. Based on this hypothesis, we propose a novel neural-learning algorithm of the self-interested agent which leverages the context and the object’s behavior. The agent learns to perform object manipulation over multiple sequences of time, using its own behavior and the object’s behavior as input. Extensive experiments are performed to demonstrate the validity of the proposed approach on various object manipulation tasks, including three-legged object manipulation, hand-categorized manipulation, automatic manipulation, and hand-held object manipulation. Using the proposed algorithm the agents are able to detect the object’s behaviors in a visual manner and automatically determine how to handle the situation using a novel, yet challenging, neural-learning algorithm.

A Bayesian Model for Sensitivity of Convolutional Neural Networks on Graphs, Vectors and Graphs

Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier

Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part Frames

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  • The R Package K-Nearest Neighbor for Image Matching

    Learning Visual Attention MechanismsThis paper presents an evolutionary algorithm for automatic object manipulation, namely, an algorithm for determining when a single object is manipulated effectively based on the observed context and on the object’s overall behavior. The proposed approach is based on the hypothesis that a single object is manipulated effectively by multiple objects. Based on this hypothesis, we propose a novel neural-learning algorithm of the self-interested agent which leverages the context and the object’s behavior. The agent learns to perform object manipulation over multiple sequences of time, using its own behavior and the object’s behavior as input. Extensive experiments are performed to demonstrate the validity of the proposed approach on various object manipulation tasks, including three-legged object manipulation, hand-categorized manipulation, automatic manipulation, and hand-held object manipulation. Using the proposed algorithm the agents are able to detect the object’s behaviors in a visual manner and automatically determine how to handle the situation using a novel, yet challenging, neural-learning algorithm.


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