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VideoModalitiesML

A set of ML/DL pipelines and models used to analyse various modalities of video data.

These pipelines have been tested on/ used to analyse the First Impressions V2 dataset(CVPR' 17), http://chalearnlap.cvc.uab.es/dataset/24/description/

Modalities

Audio

Audio_Features_Model.ipynb

  • Input :- Requires audio features (CSV file) extracted using Librosa or any other audio feature extracting library.
  • Models :- Contains implementation of Linear Regression and Random Forest models on audio features (Sklearn).

Audio_Features_Model.ipynb

  • Input :- Requires audio spectrograms using https://github.com/swharden/Spectrogram or any other such library.
  • Models :- Used the VGG11 pretrained model (Pytorch) with appended linear layers, to give the score in the desired format.

Text

Text_BOWRegression.ipynb

  • Input :- Uses the transcript data directly. No other preprocessing requires apart from feeding in the correct features from the Pandas dataframe
  • Models :- Uses the SVR and Random Forest Regressor models (Sklearn), also uses NLTK to process the text data in the notebook itself.

Text_LSTMRegression.ipynb

  • Input :- Uses the transcript data directly. No other preprocessing requires apart from feeding in the correct features from the Pandas dataframe
  • Models :- Uses a Single layer BiLSTM model. Dataloading, auxiliary preprocessing and vector embedding integration facilitated using torchtext.

Visual

Preprocessing

Use OpenCV or similar libraries to generate relevant frames from the video beforehand (Example scripts will be released soon) . The will be the inputs to the following models

Video_2d_cnn.ipynb

  • Input :- Only one representative frame will be the input here, generally used as baselines in video models.
  • Models :- Uses a pretrained 2D CNN model with appended linear layers, to give the score in the desired format.

Video_3d_cnn.ipynb

  • Input :- A set of 16 frames, in chronological order serve as input here
  • Models :- Uses a pretrained 3D CNN model (by Facebook) with appended linear layers, to give the score in the desired format.

Video_LRCN.ipynb

  • Input :- A set of 40 frames, in chronological order serve as input here
  • Models :- Uses a pretrained ResNet 50 encoder model followed by an LSTM decoder with appended linear layers, to give the score in the desired format.

Output and Metrics

As these models/pipelines have been trained on the First Impressions V2 dataset, the output is in the form of IOCEAN traits (Interview score + Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism traits). These were given as a real value between 0-1.

The loss used in the Deep Learning models is generally L1 or L2 (MSE) loss. Since we have used pytorch, adapting the code to a different loss function should be as easy as changing the function call.

The metric used is 1-MAE (Mean absolute error), used here http://chalearnlap.cvc.uab.es/dataset/24/results/49/

On issues

Feel free to post issues if you find a bug and/or to suggest changes to the pipelines or models.