Multimodal data classification

Tiya Vaj
2 min readApr 5, 2024

Dealing with multimodal data classification tasks involves a few key challenges and strategies. Here’s a breakdown of how to approach it:

Understanding Multimodal Data

Multimodal data combines multiple modalities (data types), such as:

  • Text: Words, sentences, articles
  • Image: Photographs, diagrams, illustrations
  • Audio: Speech, music, sound effects
  • Video: Combination of images and often audio

Key Challenges and Strategies:

  1. Representation and Fusion: Finding suitable representations for each modality and fusing them effectively is fundamental. Strategies include:
  • Feature Extraction: Extract relevant features from each modality using modality-specific techniques (text embeddings, image features, audio spectrograms, etc.).
  • Early Fusion: Combine raw data or low-level features from different modalities.
  • Late Fusion: Make predictions based on each modality separately, then combine the results.
  • Hybrid Fusion: Combine different fusion approaches.

2.Heterogeneity: Modalities differ in dimensionality, structure, and meaning.

  • Normalization and Preprocessing: Scale data for compatibility and use specialized preprocessing for each modality (e.g., text tokenization, image resizing).

3.Alignment: Aligning information across modalities, finding correlations between words and image regions or video snippets and sound. Attention mechanisms are often used for fine-grained alignment.

Common Approaches:

  • Transformer-Based Architectures: Transformers are highly effective in processing and relating sequential data across modalities.
  • Joint Embeddings: Learn a common embedding space to represent information from different modalities.
  • Multimodal Deep Neural Networks: Design specialized neural network architectures to handle multiple modalities.

Example: Image-Text Classification

  1. Feature Extraction:
  • Use a pre-trained CNN (like ResNet) to extract image features.
  • Employ a text embedding model (like BERT) to represent the text.
  • 2.Fusion:
  • Concatenate the extracted features and feed them to a classification layer (for early fusion).
  • Train separate models on image and text, then combine the predictions (for late fusion).

Optional: Attention Mechanism: Add an attention layer to focus on the most relevant parts of the image based on the text, refining the classification.

Tools and Libraries:

  • PyTorch, TensorFlow: Popular deep learning frameworks with multimodal capabilities.
  • HuggingFace Transformers: Provides pre-trained models for various modalities.
  • Specialized libraries (e.g., MMF by Facebook AI) for multimodal research.

Important Considerations

  • Availability of Data: Multimodal datasets can be harder to find and curate.
  • Computational Cost: Training multimodal models is often resource-intensive.
  • Model Choice: Careful selection of architectures and fusion strategies is crucial for optimal results.



Tiya Vaj

Ph.D. Research Scholar in NLP and my passionate towards data-driven for social good.Let's connect here