• https://nuowenlei-attention-lstms-in-multimoda-model-comparison-hoq2rx.streamlit.app/

• https://drive.google.com/file/d/141EvMx3TkSuvYOnoktXEQeCwjkQ76B8c/view?usp=sharing

This repository contains all models, experiments, and results from the paper Attention LSTMs in Multimodal Models. All models and experiments are implemented and executed with TensorFlow and Keras. Below is the organization structure:

• AttentionBottleneckLSTM/ contains the overall utility file att_bott_utils.py, which has many helper functions as well as the model creation function for the Attention Bottleneck Mid Fusion Model.
  • create_att_bottleneck_model() creates the attention bottleneck mid fusion model
  • load_sequential_data() loads and transforms data into processible form for the model
  • create_flow() creates generators for model training and testing.
• Conv2DAttentionLSTM/ contains files and helper functions for the Image Attention LSTM model. In conv2d_mha_utils.py, below are the important functions.
  • create_conv_mha_lstm_model() creates an Image Attention LSTM model
  • load_sequential_data() loads and transforms data specific to the model
  • create_flow() creates generators for training and testing
• GraphAttentionLSTM/ contains files and helper functions for the Graph Attention LSTM model. In mhga_utils.py, below are the important functions.
  • create_graph_attention_lstm_model() creates a Graph Attention LSTM model
  • load_sequential_data() loads and transforms data specific to the model
  • create_flow() creates generators for training and testing
• experiments/ are the colab notebooks used for experiments
• Results_metric.xlsx contains the organized results that are also shown in the paper
• imports.py is a file that contains all library imports needed for models. Due to some modules being session-based (Ex: TensorFlow), taking all imports from a single source makes sure only one session is created.