Fast and Accurate Semantic Matching on the Segmented Skeleton with Partially-Latent Stochastic Block Models


Fast and Accurate Semantic Matching on the Segmented Skeleton with Partially-Latent Stochastic Block Models – We consider the problem of segmentation from a large-scale collection of labeled images. While the majority of existing works have explicitly applied deep learning to image segmentation, little has been learned about how it operates in real-world scenarios. In this paper, we explore this problem in the context of image classification on both synthetic benchmark datasets and real-world datasets, where we propose a novel unsupervised classification algorithm, which can automatically learn segmentations from a large-scale collection of labeled images. We demonstrate its effectiveness in a challenging classification problem where the number of labeled videos is huge, and our model is trained on a collection of labeled faces of 10,000 videos. Furthermore, we show that the proposed algorithm can automatically segment a large dataset of labeled videos and find the best segmentation solution in a real-time, real-time problem.

In this paper, we propose a novel method of inferring the model parameters given the data which is based on deep learning. We show that deep learning based models have significantly improved state-of-the-art classification accuracy, with a significant reduction in classification time. Also, deep learning based models outperform state-of-the-art methods that use hand-coded attributes. This paper gives us an opportunity to evaluate the proposed approach for various tasks like human face recognition, social interaction, etc. In experiments involving humans, we find that there is a significant reduction in the number of features which is due to the use of deep learning models and a real-time feature extraction approach.

A Hierarchical Multilevel Path Model for Constrained Multi-Label Learning

Towards a knowledge-based model for planning the emergence and progression of complex networks

Fast and Accurate Semantic Matching on the Segmented Skeleton with Partially-Latent Stochastic Block Models

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  • Constrained Deep Learning for Visual Recognition

    Multitask Learning for Knowledge Base Linking via Neural-SynthesisIn this paper, we propose a novel method of inferring the model parameters given the data which is based on deep learning. We show that deep learning based models have significantly improved state-of-the-art classification accuracy, with a significant reduction in classification time. Also, deep learning based models outperform state-of-the-art methods that use hand-coded attributes. This paper gives us an opportunity to evaluate the proposed approach for various tasks like human face recognition, social interaction, etc. In experiments involving humans, we find that there is a significant reduction in the number of features which is due to the use of deep learning models and a real-time feature extraction approach.


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