How do I use Keras for training multimodal models that combine text and images

Question

Can you tell me How do I use Keras for training multimodal models that combine text and images?

Answer 1

To train a multimodal model in Keras that combines text and images, create separate CNN (for images) and LSTM/Transformer (for text) encoders, concatenate their feature embeddings, and train a joint model for classification or regression tasks.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Uses Separate CNN & LSTM for Feature Extraction:

  • CNN processes images, while LSTM extracts sequential dependencies from text data.

• Embeds Text Features Using an Embedding Layer:

  • Converts text into dense vector representations before passing it to LSTM.

• Merges Features Using Concatenate() Layer:

  • Combines image and text embeddings for joint learning.

• Supports Custom Architectures (Transformers, ResNet, BERT):

  • Replace LSTM with BERT/Transformer and CNN with ResNet/Inception for better results.

• Trains on Multimodal Data for Better Predictions:

  • Useful for image-captioning, visual question answering, and medical AI.

Hence, Keras enables multimodal learning by fusing CNN (for images) and LSTM/Transformers (for text), allowing models to understand and generate predictions based on multiple data modalities.