Fast Multi-scale Deep Learning for Video Classification


Fast Multi-scale Deep Learning for Video Classification – In a nutshell, we propose a simple, yet effective method for online feature extraction in video. The main idea is to extract a set of features into the hidden variable space without using any external knowledge. We show results on both human and machine learning datasets that show that the proposed method achieves competitive predictions in a variety of video contexts, and that it outperforms state-of-the-art methods by a significant margin.

We propose a probabilistic model-based approach to the problem of Chinese character recognition. In this paper we formalize our approach, and present two generalization algorithms for the Chinese character recognition problem. (1) the method is able to exploit both structural similarities and different character pairs. We demonstrate that a new approach, the two-class method, can be used for the classification of Chinese characters without using any structural similarity between character pairs. (2) the method can capture the character pairs that are shared and then predict their position in class. This is demonstrated on multiple Chinese character recognition benchmarks. The results indicate that our method is more suitable for character recognition because the two-class method is more suitable for character recognition.

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Fast Multi-scale Deep Learning for Video Classification

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  • Video Description Based on Spatial Context with Applications to Speech Recognition

    A Context-based Approach for Novel Chinese Character Classification Using Tree-Leaf ClassificationWe propose a probabilistic model-based approach to the problem of Chinese character recognition. In this paper we formalize our approach, and present two generalization algorithms for the Chinese character recognition problem. (1) the method is able to exploit both structural similarities and different character pairs. We demonstrate that a new approach, the two-class method, can be used for the classification of Chinese characters without using any structural similarity between character pairs. (2) the method can capture the character pairs that are shared and then predict their position in class. This is demonstrated on multiple Chinese character recognition benchmarks. The results indicate that our method is more suitable for character recognition because the two-class method is more suitable for character recognition.


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