Cross-Modal Retrieval

Cross-modal retrieval is the defining feature of Multimodal RAG. It breaks the barrier between text-only databases and multi-media assets.

Semantic Bridging

In a cross-modal system, we don't just search for text; we search for ideas across formats.

Use Case 1: Video Search

User Query: "Show me the part of the presentation where they discuss the roadmap."

1. Search Audio: Look for the word "roadmap" in the transcripts.
2. Search Visuals: Look for the visual concept of a "timeline" or "gantt chart" in the video frames using CLIP.
3. Joint Rank: Show the segment that matches both visual and audio signals.

Use Case 2: Visual Evidence

User Query: "Find photos of broken solar panels."

1. Model: Use a multimodal model (like CLIP or Google Titan).
2. Retrieve: Find images whose vector is close to the text vector for "broken solar panels".

Implementation Strategy: Unified Collections

You can store text and images in the same Chroma collection if they were embedded by the same model (like CLIP).

# Searching a unified collection
results = collection.query(
    query_texts=["red truck in the desert"],
    n_results=5
)
# Results might include both descriptions of trucks AND actual image files.

The "Dual-Encoder" Approach

Most cross-modal systems use a dual-encoder architecture:

• One encoder for Text.
• One encoder for Images/Video/Audio.
• Both encoders are trained to put related content in the same spot in a shared vector space.

Challenges in Cross-Modal Retrieval

1. Resolution Mismatch: A text query might be highly specific ("1998 Red Civic"), but the image embedding might only capture "Red Car".
2. Modality Bias: Sometimes the model is "better" at text than images, leading to uneven results.

Exercises

1. Find an image of a "Modern Kitchen."
2. Use a CLIP model to find the most semantically similar piece of furniture from a list of descriptions (e.g., "Steel Fridge", "Wooden Chair", "Linen Bedding").
3. Why is cross-modal retrieval more "Human-like" than traditional keyword search?