Introduction

The image encoder is a vital component of the multimodal AI model, responsible for processing and interpreting visual data. It converts raw images into meaningful representations that the model can understand and utilize alongside text, audio, and animation data. This section provides a comprehensive overview of the image encoder, including its architecture, functionality, and integration within the multimodal framework.

Role and Importance

The image encoder enables the AI to understand and process visual information, such as images and videos. This capability is crucial for applications involving image recognition, object detection, scene understanding, and more.

Key Functions

1. Preprocessing: Normalizes and resizes raw images to prepare them for further processing.
2. Feature Extraction: Converts images into high-dimensional feature vectors.
3. Contextualization: Refines these features by capturing spatial dependencies within the image data.
4. Integration: Ensures that image representations are compatible with other modalities in the model.

Architecture

The image encoder is typically built using convolutional neural networks (CNNs) or transformer-based models like Vision Transformers (ViTs). These architectures are designed to capture both local and global patterns in the visual data.

Convolutional Layers

The convolutional layers are responsible for extracting low-level features from the raw image. These features can include edges, textures, and simple shapes.

• Convolutional Filters: Apply filters to the image to detect local patterns and features.
• Pooling Layers: Reduce the dimensionality of the feature maps, preserving important information while reducing computational complexity.

Transformer Layers (Vision Transformers)

Vision Transformers (ViTs) apply transformer architecture to images, treating image patches as sequences. This approach captures long-range dependencies and global context more effectively than traditional CNNs.

• Patch Embedding: Divides the image into fixed-size patches and embeds each patch into a high-dimensional vector.
• Self-Attention Mechanism: Allows the model to focus on relevant parts of the image, capturing dependencies across different patches.

Embedding Layer