Multimodal Learning
Multimodal learning involves training models on multiple types of data (text, images, audio, video). This enables tasks like text‑to‑image generation, visual question answering, and video captioning.
Key Concepts
- Shared embedding space: map different modalities into a common representation where similar concepts are close (e.g., image of a dog and text "dog" have similar vectors).
- Contrastive learning: train to pull matching pairs together, push non‑matching apart.
CLIP (Contrastive Language–Image Pre‑training)
CLIP, developed by OpenAI, learns a joint embedding space for images and text. It enables zero‑shot image classification: given an image, compare its embedding with text descriptions of classes. Also used for text‑to‑image generation (e.g., DALL‑E, Stable Diffusion).
DALL‑E
DALL‑E (and later versions) generates images from text prompts. It uses a Transformer trained on text‑image pairs. DALL‑E 3 achieves high prompt fidelity.
Other Multimodal Models
- Flamingo (DeepMind): visual question answering, few‑shot image recognition.
- BLIP: image captioning, vision‑language understanding.
- LLaVA: vision‑language model based on Llama.
- GPT‑4V (GPT‑4 with vision): multimodal LLM that accepts images.
Applications
- Text‑to‑image generation (DALL‑E, Midjourney, Stable Diffusion).
- Image captioning, visual question answering.
- Video understanding and captioning.
- Multimodal chatbots that can see and read.
Two Minute Drill
- Multimodal learning combines text, images, audio, etc.
- CLIP creates shared text‑image embeddings.
- DALL‑E generates images from text.
- Used for captioning, VQA, multimodal chatbots.
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