How Does Vision Meet Language in Modern AI Systems?

Workflow of Vision Language Models in 6 simple steps

Vision-Language Models (VLMs) combine computer vision and natural language processing (NLP) to understand and generate information across images and text. These models power applications like image captioning, visual question answering (VQA), and multimodal search.

Step1: Data Collection & Preprocessing

Before training, the model processes image-text pairs:

• Image Processing: Resizing, normalizing, and extracting features using CNNs or Vision Transformers.
• Text Processing: Tokenizing text, converting words into embeddings, and aligning with images.

Step2: Feature Extraction

To convert raw data into structured embeddings:

• Vision Encoder (e.g., ViT, ResNet): Extracts high-dimensional image features.
• Text Encoder (e.g., BERT, GPT): Converts text into vector representations.

Step3:  Multimodal Fusion

The model integrates visual and text features through:

• Early Fusion: Merges image and text at input.
• Late Fusion: Combines outputs after separate processing.
• Cross-Attention Mechanism: Enables interaction between modalities.

Step 4: Model Training

VLMs learn using:

• Contrastive Learning (e.g., CLIP): Aligns correct image-text pairs.
• Masked Modeling (e.g., BLIP): Hides and Trains to predict the missing parts of image or text.
• Supervised Learning: Trains on labeled datasets for specific tasks.
• Reinforcement Learning: Fine-tune model based on reward ( human feedback)

Step 5: Inference (Prediction)

The trained model generates responses for tasks like:

• Image Captioning: "Man eating a burger."
• Visual Question Answering (VQA): Answering questions about an image.
• Multimodal Search: Retrieving images based on a text query.

Step 6: Post-Processing & Output

Final refinements ensure accuracy and usability:

• Refinement: Filters low-confidence results.
• Ranking: Selects the best output.
• Formatting: Structures responses for applications.

The 6-step VLM workflow integrates image and text data through preprocessing, feature extraction, fusion, training, and inference, enabling machines to understand multimodal content effectively.

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