Playing with Vision Embeddings
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Playing with Vision Embeddings
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Re: Playing with Vision Embeddings
#2Re: Playing with Vision Embeddings
#3One thing I still struggle with in my head is how these vision embeddings can then be used to give LLMs eyes.
Because you somehow need a giant training set which describes images in natural language, no? Is that actually how it works, or is there some smart trick so you don't need to pay labellers a bunch of money to look at pictures and describe them.
Re: Playing with Vision Embeddings
#4Very nice visualizations, thanks for that! One thing I still struggle with in my head is how these vision embeddings can then be used to give LLMs eyes. Because you somehow need a giant training set which describes images in natural language, no? Is that actually how it works, or is there some smart trick so you don't need to pay labellers a bunch of money to look at pictures and describe them.
That's definitely one way - they train a text encoder together with an image encoder on a labelled set of images. WL & 3b1b made a nice video on it: https://www.youtube.com/watch?v=iv-5mZ_9CPY
Re: Playing with Vision Embeddings
#5Very nice visualizations, thanks for that! One thing I still struggle with in my head is how these vision embeddings can then be used to give LLMs eyes. Because you somehow need a giant training set which describes images in natural language, no? Is that actually how it works, or is there some smart trick so you don't need to pay labellers a bunch of money to look at pictures and describe them.
> Because you somehow need a giant training set which describes images in natural language, no? That's definitely one way - they train a text encoder together with an image encoder on a labelled set of images. WL & 3b1b made a nice video on it: https://www.youtube.com/watch?v=iv-5mZ_9CPY
Re: Playing with Vision Embeddings
#6Re: Playing with Vision Embeddings
#7One thought on your nicely illustrated "key observation [is] that neural networks tend to place features along directions": my guess is that the neural net was TOLD to behave that way by choosing e.g. Cosine Loss?