multimodal deep learning jiquan ngiam aditya khosla, mingyu kim, juhan nam, honglak lee & andrew...
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Multimodal Deep LearningJiquan NgiamAditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Stanford University
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
McGurk Effect
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Audio-Visual Speech Recognition
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Feature Challenge
Classifier (e.g. SVM)
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Representing Lips
• Can we learn better representations for audio/visual speech recognition?
• How can multimodal data (multiple sources of input) be used to find better features?
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Unsupervised Feature Learning
51.1
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10
91.67
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3
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Unsupervised Feature Learning
51.1
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109
1.67...
3
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Multimodal Features
12.159.......
6.59
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Cross-Modality Feature Learning
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10
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Feature Learning Models
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Feature Learning with Autoencoders
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...Audio Input
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...Video Input
... ...Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Autoencoder
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Audio Input Video Input
HiddenRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Autoencoder
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... ...
Audio Input Video Input
HiddenRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Shallow Learning H
idde
n U
nits
Video Input Audio Input
• Mostly unimodal features learned
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Autoencoder
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Audio Input Video Input
HiddenRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Autoencoder
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...
... ...
Video Input
HiddenRepresentation
Audio Reconstruction Video Reconstruction
Cross-modality Learning: Learn better video features by using audio as a cue
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Cross-modality Deep Autoencoder
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Video Input
LearnedRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Cross-modality Deep Autoencoder
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Audio Input
LearnedRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Deep Autoencoders
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... ...
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Audio Input Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
“Visemes”(Mouth Shapes)
“Phonemes”
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Deep Autoencoders
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Video Input
Audio Reconstruction Video Reconstruction
“Visemes”(Mouth Shapes)
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
“Phonemes”
Bimodal Deep Autoencoders
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Audio Input
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Deep Autoencoders
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... ...
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Audio Input Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
“Visemes”(Mouth Shapes)
“Phonemes”
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Training Bimodal Deep Autoencoder
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Audio Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
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Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
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Audio Input Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
• Train a single model to perform all 3 tasks
• Similar in spirit to denoising autoencoders(Vincent et al., 2008)
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Evaluations
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Visualizations of Learned Features
0 ms 33 ms 67 ms 100 ms
0 ms 33 ms 67 ms 100 ms
Audio (spectrogram) and Video features learned over 100ms windows
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with AVLetters
• AVLetters: – 26-way Letter Classification– 10 Speakers– 60x80 pixels lip regions
• Cross-modality learning
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Video Input
LearnedRepresentation
Audio Reconstruction Video Reconstruction
Feature Learning Supervised Learning Testing
Audio + Video Video Video
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with AVLetters
Feature Representation Classification Accuracy
Multiscale Spatial Analysis (Matthews et al., 2002)
44.6%
Local Binary Pattern(Zhao & Barnard, 2009)
58.5%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with AVLetters
Feature Representation Classification Accuracy
Multiscale Spatial Analysis (Matthews et al., 2002)
44.6%
Local Binary Pattern(Zhao & Barnard, 2009)
58.5%
Video-Only Learning(Single Modality Learning) 54.2%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with AVLetters
Feature Representation Classification Accuracy
Multiscale Spatial Analysis (Matthews et al., 2002)
44.6%
Local Binary Pattern(Zhao & Barnard, 2009)
58.5%
Video-Only Learning(Single Modality Learning) 54.2%
Our Features(Cross Modality Learning) 64.4%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with CUAVE
• CUAVE: – 10-way Digit Classification– 36 Speakers
• Cross Modality Learning.........
...... ......
Video Input
LearnedRepresentation
Audio Reconstruction Video Reconstruction
Feature Learning Supervised Learning Testing
Audio + Video Video Video
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with CUAVE
Feature Representation Classification Accuracy
Baseline Preprocessed Video 58.5%Video-Only Learning
(Single Modality Learning) 65.4%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with CUAVE
Feature Representation Classification Accuracy
Baseline Preprocessed Video 58.5%Video-Only Learning
(Single Modality Learning) 65.4%
Our Features(Cross Modality Learning) 68.7%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Lip-reading with CUAVE
Feature Representation Classification Accuracy
Baseline Preprocessed Video 58.5%Video-Only Learning
(Single Modality Learning) 65.4%
Our Features(Cross Modality Learning) 68.7%
Discrete Cosine Transform(Gurban & Thiran, 2009)
64.0%
Visemic AAM(Papandreou et al., 2009)
83.0%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Multimodal Recognition
• CUAVE: – 10-way Digit Classification– 36 Speakers
• Evaluate in clean and noisy audio scenarios– In the clean audio scenario, audio performs
extremely well alone
Feature Learning Supervised Learning Testing
Audio + Video Audio + Video Audio + Video
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Audio Input Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Multimodal Recognition
Feature Representation Classification Accuracy(Noisy Audio at 0db SNR)
Audio Features (RBM) 75.8%Our Best Video Features 68.7%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Multimodal Recognition
Feature Representation Classification Accuracy(Noisy Audio at 0db SNR)
Audio Features (RBM) 75.8%Our Best Video Features 68.7%
Bimodal Deep Autoencoder 77.3%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Multimodal Recognition
Feature Representation Classification Accuracy(Noisy Audio at 0db SNR)
Audio Features (RBM) 75.8%Our Best Video Features 68.7%
Bimodal Deep Autoencoder 77.3%
Bimodal Deep Autoencoder + Audio Features (RBM) 82.2%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Shared Representation Evaluation
SupervisedTesting
Audio
SharedRepresentation
Video Audio
SharedRepresentation
Video
Linear Classifier
Training Testing
Feature Learning Supervised Learning Testing
Audio + Video Audio Video
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Shared Representation Evaluation
SupervisedTesting
Audio
SharedRepresentation
Video Audio
SharedRepresentation
Video
Linear Classifier
Training Testing
• Method: Learned Features + Canonical Correlation Analysis
Feature Learning Supervised Learning Testing Accuracy
Audio + Video Audio Video 57.3%Audio + Video Video Audio 91.7%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
McGurk Effect
A visual /ga/ combined with an audio /ba/ is often perceived as /da/.
AudioInput
VideoInput
Model Predictions
/ga/ /ba/ /da/
/ga/ /ga/ 82.6% 2.2% 15.2%
/ba/ /ba/ 4.4% 89.1% 6.5%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
McGurk Effect
A visual /ga/ combined with an audio /ba/ is often perceived as /da/.
AudioInput
VideoInput
Model Predictions
/ga/ /ba/ /da/
/ga/ /ga/ 82.6% 2.2% 15.2%
/ba/ /ba/ 4.4% 89.1% 6.5%
/ga/ /ba/ 28.3% 13.0% 58.7%
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Conclusion
• Applied deep autoencoders to discover features in multimodal data
• Cross-modality Learning: We obtained better video features (for lip-reading) using audio as a cue
• Multimodal Feature Learning:Learn representations that relate across audio and video data
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Video Input
LearnedRepresentation
Audio Reconstruction Video Reconstruction
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Audio Input Video Input
SharedRepresentation
Audio Reconstruction Video Reconstruction
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee & Andrew Ng
Bimodal Learning with RBMs
…......
Audio Input
Hidden Units
...Video Input
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