Multimodal AI combines multiple types of data, such as text and images to enable richer, more comprehensive AI capabilities. When applied to images, it connects the visual understanding of scenes, objects, and attributes with the contextual insights derived from text. This integration allows AI systems to perform tasks that require reasoning across modalities, such as generating captions for images, answering questions about visual content, or even synthesising entirely new visuals from text prompts.

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Key Components of Multimodal AI

  1. Visual Feature Extraction
    • Visual inputs are processed using deep learning architectures such as convolutional neural networks (CNNs), vision transformers (ViTs), or hybrid models. These models extract meaningful features like object shapes, textures, spatial arrangements, and scene dynamics.
    • Image features are encoded into high-dimensional vectors that represent the visual attributes of the image.
  2. Textual Understanding
    • By using natural language processing to analyse the accompanying text, be it instructions, captions, or queries, to extract semantic meaning, intent, and structure.
    • Text inputs are tokenised and passed through LLM models, which encode them into embeddings.
    • These embeddings capture the relationships between words, phrases, and the overall context.
  3. Unification of Textual and Visual Understanding
    • The concatenation of visual and textual embeddings to create a unified understanding of the input, capturing the relationships between modalities.
  4. Output Generation
    • The production of outputs such as captions, answers, or synthesised images based on the concatenated embeddings of inputs.

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Ensuring Reliable Multimodal System

The effectiveness of multimodal AI lies in its ability to produce outputs that accurately reflect the alignment between visual and textual inputs. However, ensuring this alignment requires robust evaluation mechanisms. Evaluating multimodal AI outputs, particularly for image-based tasks, is essential to:

  • Validate Output Quality: Ensure that generated images, captions, or responses align with user instructions or expectations.
  • Detect Errors and Misalignment: Identify discrepancies such as hallucinations, irrelevant content, or inaccurate object representations.
  • Enhance System Reliability: Provide actionable insights to refine the performance of multimodal AI systems over time.

To maintain high standards for these systems, evaluation methods focus on assessing alignment, consistency, and logical coherence between inputs and outputs.

Click here to learn how to evaluate your image-based multimodal system