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This repository contains the code for the paper `End-to-End Multimodal Emotion Recognition using Deep Neural Networks`.
| Date | Stars |
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| 2026-07-31 | 253 |
| 2026-08-03 | 253 |
| 2026-08-04 | 253 |
| 2026-08-06 | 253 |
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# End-to-End Multimodal Emotion Recognition using Deep Neural Networks This package provides training and evaluation code for the end-to-end multimodal emotion recognition paper. If you use this codebase in your experiments please cite: `P. Tzirakis, G. Trigeorgis, M. A. Nicolaou, B. Schuller and S. Zafeiriou, "End-to-End Multimodal Emotion Recognition using Deep Neural Networks," in IEEE Journal of Selected Topics in Signal Processing, vol. PP, no. 99, pp. 1-1.` (http://ieeexplore.ieee.org/document/8070966/) ## UPDATE ### Implementation of this method in PyTorch (along with pretrain models) can be found in our [End2You toolkit](https://github.com/end2you/end2you) ## Requirements Below are listed the required modules to run the code. * Python <= 2.7 * NumPy >= 1.11.1 * TensorFlow <= 0.12 * Menpo >= 0.6.2 * MoviePy >= 0.2.2.11 ## Content This repository contains the files: * model.py: contains the audio and video networks. * emotion_train.py: is in charge of training. * emotion_eval.py: is in charge of evaluating. * data_provider.py: provides the data. * data_generator.py: creates the tfrecords from '.wav' files * metrics.py: contains the concordance metric used for evaluation. * losses.py: contains the loss function of the training. * inception_processing.py: provides functions for visual regularization. The multimodal model can be downloaded from here : https://www.doc.ic.ac.uk/~pt511/emotion_recognition_model.zip
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8eb41dcc7fc1b2a6, name:multimodal, desc:multimodal