Software Engineers Can Learn From Audio-Visual Representation Models
Computers learn to connect images & sounds with self-supervised technique, separating "chirp" (env sounds) from "chat" (speech), enabling better understanding of multimodal world.
This is a Plain English Papers summary of a research paper called Separating the Chirp from the Chat: Self-supervised Visual Grounding of Sound and Language. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview This paper introduces a self-supervised approach for learning visual representations from audio-visual correspondence. The method aims to separate "chirp" (environmental sounds) from "chat" (speech) and learn visual representations that are grounded in both types of audio. The learned representations can be...