Summary of Multimodal AI from First Principles - Neural Nets that can see, hear, AND write.

Multimodal models integrate visual inputs with text to perform tasks like text to image or video retrieval, visual grounding, visual question answering, and visual dialog.

Multimodal learning involves learning dependencies and relationships between different input types to align them in a joint representation space.

Unimodal embeddings are formed by processing each input modality separately with appropriate neural networks like RNNs or Transformers.

Fusion steps combine these unimodal embeddings into a joint representation space for cross-model interactions.

Training tasks like contrastive learning, discriminative training, and masked visual language modeling align multimodal data sets.

Research papers like Blip, Hero, and VLT5 focus on unified modeling to train a single model on diverse tasks for generalized embeddings.

Frozen People addressed LLN fine-tuning challenges by freezing LLN weights and training vision encoders separately.

Flamingo integrates pre-trained text and vision encoders using perceiver networks and gated cross-attention layers for multimodal tasks.

Mini GBD4 and Cosmos 1 demonstrate few-shot learning capabilities for tasks like captioning images, IQ tests, and visual question answering.

Palm e by Google uses embodied language modeling for robotics tasks by combining language instructions with physical presence.

Generative multimodal language models allow users to condition them through visual and textual prompting for retaining vast knowledge.

Researchers/sources

Notable Quotes

— 13:54 — « Wouldnt it be really cool if we could transfer all that knowledge of a generative text model into a multimodal setting? »
— 15:03 — « Frozen showed some really cool zero-shot transfer to multiple tasks on unseen data like identifying objects or answering questions about the images. »
— 17:45 — « Prompting the model with images to read sign boards, answering General Knowledge Questions, predicting the future of a video sequence, responding in a foreign language, and engaging in visual dialogue shows The Power of Few-shot learning. »

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Science and Nature

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