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Daniel Liu · Daniel Liu

This is Daniels Blog. Thu, Jun 9, 2016. Sequence to Sequence Video to Text. This paper mainly proposes a end-to-end method to translate videos into text descriptions. It uses CNN for feature extraction and LSTM for encoding and decoding of the features and word representations. The main framework of the S2VT system is shown below:. Video and text representation. The hyper-parameter alpha is tuned on the validation set. Wed, May 25, 2016. Deep Neural Networks for Acoustic Modelling in Speech Recognition.

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Daniel Liu · Daniel Liu | mithril-ntu.github.io Reviews

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This is Daniels Blog. Thu, Jun 9, 2016. Sequence to Sequence Video to Text. This paper mainly proposes a end-to-end method to translate videos into text descriptions. It uses CNN for feature extraction and LSTM for encoding and decoding of the features and word representations. The main framework of the S2VT system is shown below:. Video and text representation. The hyper-parameter alpha is tuned on the validation set. Wed, May 25, 2016. Deep Neural Networks for Acoustic Modelling in Speech Recognition.

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2 hd lbp

3 l2 paper

4 l3 paper

5 l4 paper

6 l5 paper

7 l6 paper

8 l7 paper

9 l8 paper

10 welcome

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Daniel Liu · Daniel Liu | mithril-ntu.github.io Reviews

https://mithril-ntu.github.io

This is Daniels Blog. Thu, Jun 9, 2016. Sequence to Sequence Video to Text. This paper mainly proposes a end-to-end method to translate videos into text descriptions. It uses CNN for feature extraction and LSTM for encoding and decoding of the features and word representations. The main framework of the S2VT system is shown below:. Video and text representation. The hyper-parameter alpha is tuned on the validation set. Wed, May 25, 2016. Deep Neural Networks for Acoustic Modelling in Speech Recognition.

INTERNAL PAGES

mithril-ntu.github.io

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L7_PAPER · Daniel Liu

http://mithril-ntu.github.io/L7_PAPER

This is Daniels Blog. Tue, Apr 19, 2016. Two-Stream Convolutional Networks for Action Recognition in Videos. The main contribution of this paper can be divided into 3 parts: a). Propose a new two stream(spatial and temporal streams) ConvNet architecture for action recognition; b). Prove that a ConvNet trained on multi-frame dense optical flow can achieve good performance even if there are little training data; c). Two stream ConvNet architecture. In this ConvNet, the only difference with the spatial Conv...

2

L14_PAPER · Daniel Liu

http://mithril-ntu.github.io/L14_PAPER

This is Daniels Blog. Thu, Jun 9, 2016. Sequence to Sequence Video to Text. This paper mainly proposes a end-to-end method to translate videos into text descriptions. It uses CNN for feature extraction and LSTM for encoding and decoding of the features and word representations. The main framework of the S2VT system is shown below:. Video and text representation. The hyper-parameter alpha is tuned on the validation set.

3

L5_PAPER · Daniel Liu

http://mithril-ntu.github.io/L5_PAPER

This is Daniels Blog. Mon, Mar 21, 2016. Nonlinear Dimensionality Reduction by Locally Linear Embedding. How to discover compact representations of high-dimensional data, which can reserve the manifold structure of original data. Locally Linear Embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Based on this intuition, the author defines a cost function:. Two-dimensional embeddings based on LLE of faces and wor...

4

Welcome · Daniel Liu

http://mithril-ntu.github.io/welcome

This is Daniels Blog. Sat, Mar 12, 2016.

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HD_LBP · Daniel Liu

http://mithril-ntu.github.io/HD_LBP

This is Daniels Blog. Sun, Apr 17, 2016. Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification. This paper has two main contributions:. A)Show that high dimensionality is critical to high performance. B)Propose a sparse projection method, named rotated sparse regression. By adding a rotation matrix R, this becomes to:. Though fixing R and B in turn, we can optimize them iteratively. Finally, we can get a linear projection matrix B with additional fre...

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csie.ntu.edu.tw

AMMAI - ADVANCED TOPICS IN MULTIMEDIA ANALYSIS AND INDEXING (高等多媒體資訊分析與檢索)

http://www.csie.ntu.edu.tw/~winston/courses/ammai/index.html

922 U3710: AMMAI - ADVANCED TOPICS IN MULTIMEDIA ANALYSIS AND INDEXING. Spring 2016 (14:20 17:20, Thursday, CSIE RM#542). Advanced image features (e.g., local features, shapes, etc.). Advanced topics for large-scale image retrieval. Deep neural networks and its applications in image, text, and speech. Automatic image and video annotation. Automatic visual training data acquisition. Gaining practical experiences through assignments and experiments. Office: R512, CSIE Building). Sagethen -at- gmail.com.

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mithril-ntu.github.io

Daniel Liu · Daniel Liu

This is Daniels Blog. Thu, Jun 9, 2016. Sequence to Sequence Video to Text. This paper mainly proposes a end-to-end method to translate videos into text descriptions. It uses CNN for feature extraction and LSTM for encoding and decoding of the features and word representations. The main framework of the S2VT system is shown below:. Video and text representation. The hyper-parameter alpha is tuned on the validation set. Wed, May 25, 2016. Deep Neural Networks for Acoustic Modelling in Speech Recognition.

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