Discovery of Nonlinear Structural Relations with Hierarchical Feature Priors


Discovery of Nonlinear Structural Relations with Hierarchical Feature Priors – This paper presents a Bayesian inference framework for Bayesian regression on graphs based on a Bayesian kernel density model. The main contribution of this paper is a new Bayesian estimation method for hierarchical clustering in graphs. The Bayesian kernel density model is derived from a logistic regression algorithm that is based on the spectral clustering of node segments. This method is compared to several similar methods for hierarchical clustering in graphs. The results show that the proposed method provides a better performance than these two approaches while having a lower computational cost.

In this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.

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Discovery of Nonlinear Structural Relations with Hierarchical Feature Priors

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    Unsupervised Domain Adaptation for Object DetectionIn this work, we propose a semi-supervised learning algorithm to learn image features based on a knowledge set from video representations. This knowledge is learned automatically from the learned feature vectors by using a combination of discriminative and discriminative filtering, and the algorithm is trained using a supervised model trained jointly with the video model and deep neural network. Our proposed semi-supervised classification algorithm combines two different aspects: (i) model-free features learning and video feature learning; (ii) feature learning and deep neural network learning (DNN) based learning. The proposed model-free classifier learns the semantic object from the visual scene, and the video representation from this semantic object. The algorithm is trained using a supervised model trained jointly with the video model and DNN. Experiments on various datasets demonstrate that our algorithm outperforms the state-of-the-art semi-supervised classifiers both in the learning task and the embedding task.


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