AI News, BOOK REVIEW: Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks - Official Project Page

Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks - Official Project Page

This repository contains the code developed by TensorFlow for the following paper:

If you used this code, please kindly consider citing the following paper:

The essential problem is to find the correspondence between the audio and visual streams, which is the goal of

modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal

The proposed architecture will incorporate both spatial and temporal information jointly to effectively

directory: The run the dedicated python file as below: Running the aforementioned script extracts the lip motions by saving the mouth area

been defined in the file: Some of the defined arguments have their default values and no further action is required

In the visual section, the videos are post-processed to have an equal frame rate of 30 f/s.

Then, face tracking and mouth area extraction are performed on the videos using the dlib

Finally, all mouth areas are resized to have the same size and concatenated to form the input feature cube.

The proposed architecture utilizes two non-identical ConvNets which uses a pair of speech and video streams.

of audio corresponds with a lip motion clip within the desired stream duration.

Each input feature map for a single audio stream has the dimensionality of 15 × 40 × 3. This

the visual network, the lip motions spatial information alongside the temporal information are incorporated

Then, cd to the dedicated directory: Finally, the file must be executed: For evaluation phase, a similar script must be executed: The below results demonstrate effects of the proposed method on the accuracy and

The current version of the code does not contain the adaptive pair selection method proposed by 3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition paper.

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