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Aff-Wild-models
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# Aff-Wild-models ## References If you use any of the models/weights, please cite the following papers: 1. D. Kollias, et. al.: "Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond". International Journal of Computer Vision (2019). > @article{kollias2019deep, title={Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond}, author={Kollias, Dimitrios and Tzirakis, Panagiotis and Nicolaou, Mihalis A and Papaioannou, Athanasios and Zhao, Guoying and Schuller, Bj{\"o}rn and Kotsia, Irene and Zafeiriou, Stefanos}, journal={International Journal of Computer Vision}, pages={1--23}, year={2019}, publisher={Springer} } 2. S. Zafeiriou, et. al. "Aff-Wild: Valence and Arousal in-the-wild Challenge", CVPRW, 2017. >@inproceedings{zafeiriou2017aff, title={Aff-wild: Valence and arousal ‘in-the-wild’challenge}, author={Zafeiriou, Stefanos and Kollias, Dimitrios and Nicolaou, Mihalis A and Papaioannou, Athanasios and Zhao, Guoying and Kotsia, Irene}, booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on}, pages={1980--1987}, year={2017}, organization={IEEE} } 3. D. Kollias, et. al. "Recognition of affect in the wild using deep neural networks", CVPRW, 2017. >@inproceedings{kollias2017recognition, title={Recognition of affect in the wild using deep neural networks}, author={Kollias, Dimitrios and Nicolaou, Mihalis A and Kotsia, Irene and Zhao, Guoying and Zafeiriou, Stefanos}, booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on}, pages={1972--1979}, year={2017}, organization={IEEE} } ## Pre-trained models: The models on Aff-Wild can be downloaded from [here](https://drive.google.com/open?id=1xkVK92XLZOgYlpaRpG_-WP0Elzg4ewpw). ## Description: The above link contains 3 folders named: "affwildnet-vggface-gru" , "affwildnet-resnet-gru" and "vggface". The "vggface" folder contains two subfolders with 2 different models: both models are CNN networks based on VGG-FACE (with 3 fully connected layers with: i) 4096, 2000, 2 and ii) 4096, 4096, 2 units, respectively). The "affwildnet-vggface-gru" folder contains the AffWildNet architecture (with no landmarks) as described in the paper entitled: ["Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond"](https://arxiv.org/pdf/1804.10938.pdf). The "affwildnet-resnet-gru" folder contains the AffWildNet architecture (with no landmarks and no fully connected layer; a Resnet-50 followed by a GRU network) as described in the paper entitled: ["Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond"](https://arxiv.org/pdf/1804.10938.pdf). Inside each of those folders, one can find the architectures of the networks, implemented in the Tensorflow environment and a readme explaining how to build/use them. An evaluation file is also uploaded with detailed explanation inside. The Aff-Wild database can be downloaded from [here](https://drive.google.com/file/d/1A6uU4XdO11o_VYSV5RCGiwCb9l4Wgk4s/view?usp=sharing). Specific details about the database and related stuff can be read in the Challenge's [site](https://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge/). ## Prerequisites: - The code works with Tensorflow 1.8 - slim is also needed (it is incorporated within Tensorflow)
