MediaFM: Netflix's Tri-Modal AI Foundation Model for Media Understanding
Netflix needed scalable machine-level understanding of its entire content catalog — including new formats like live events and podcasts — to power recommendations, ad relevancy, and promotional asset optimization, all of which require sophisticated long-form video understanding of narrative dependencies and emotional arcs spanning entire episodes or films.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Shot boundary segmentation
A shot boundary detection algorithm segments each movie or episode into individual shots as the fundamental unit of input.
Tools used
SeqCLIPwav2vec2text-embedding-3-largeMuonAdamW
Outcome
MediaFM outperforms all baselines on all evaluated tasks, with clip retrieval improving by around 15% at each model enhancement step, and with larger gains on tasks requiring detailed narrative understanding such as ad relevancy.
What failed first
Prior models not leveraging the full multimodal signal failed to grasp content essence, and the page's ablations show that using multiple modalities without contextualization can actually hurt performance on tasks like clip popularity ranking.