In the wake of OpenAI’s recent announcement of GPT-4o, a new model that processes and generates text, audio, and images in real-time, it’s clear that the race for multimodal AI is heating up. Not to be outdone, researchers at Meta AI (FAIR) have just released a fascinating new paper introducing Chameleon, a family of early-fusion foundation models that also seamlessly blend language and vision.
In this post, we’ll do a deep dive into the Chameleon paper, exploring how it pushes the boundaries of multimodal AI in different but equally exciting ways compared to GPT-4o. We’ll also speculate a bit on what this means for the direction of research in the ML space overall. Let’s go!
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Overview
The paper is titled “Chameleon: Mixed-Modal Early-Fusion Foundation Models.” While GPT-4o seems to focus a bit more on real-time processing and generation across modalities (low latency being critical for things like audio-to-audio), Chameleon is more about learning unified representations over arbitrary sequences of image and text tokens.
This early-fusion approach, where all modalities are projected into a shared space from the start, allows Chameleon to seamlessly reason over and generate interleaved visual and…
