Multimodal systems involve working with multiple data modalities such as text, image, audio, and more. OpenAI's CLIP and DeepMind's Flamingo are significant models in the field. Flamingo extends CLIP by incorporating a language model component. Research directions in the field include incorporating more data modalities, developing multimodal systems for instruction-following, and creating adapters for more efficient multimodal training.
Table of contents
Part 1. Understanding MultimodalWhy multimodalData modalitiesMultimodal tasksPart 2. Fundamentals of Multimodal TrainingCLIP: Contrastive Language-Image Pre-trainingFlamingo: the dawns of LMMsTL;DR: CLIP vs. FlamingoPart 3. Research Directions for LMMsIncorporating more data modalitiesMultimodal systems for instruction-followingAdapters for more efficient multimodal trainingGenerating multimodal outputsConclusionEarly reviewersResources14 Impressions