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Ogawa Et Al (2018), Favorite Video Classification Based On Multimodal…

- - - - Variant of RNN that effectively captures long-term and short-term temporal data
      - Used in this research to take advantage of long-term information found across frames in video clips as well as signals in EEG
    - - RNN variant that takes a sequence data and outputs a single classification
      - An example use-case is determining whether a sentence makes readers happy or not
      - Used in this research to determine how a user reacts or feels when watching a video
    - - RNN variant the takes sequence of data as inputs and also outputs sequence of data
      - Used for problems like translation where your input is a sentence and your output is also a sentence
    - - An RNN approach where information is propagated forward in time and then backwards in time before generating output
      - Used when useful information can also be observed when observing sequence of inputs backwards
- - - - Represent WUW information
      - Inception-v3 to obtain features per frame and PCA to reduce features dimensionality to 1024
    - - Represent WUL information
      - Obtained by sampling 1024 Hz and are used raw
- - - - Usually good enough on estimating WUL
      - May fail for examples like when a user watches an action movie, a WUW based WUL classification model might predict that the user likes action movies when in fact, the user watches the movie only because of the actor.