“The big result, and one of the reasons that I was really excited about this paper, is that they blow everything else away on MMVP. I mean, 47.3, sure, that's nowhere near human accuracy, which again is 94%, but for, you know, two billion language, two billion parameter language model to be ChatGPT, that's quite the achievement.”
quote is from the automated transcript, cleaned for reading:
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More from Peter Robicheaux
AssertionSupported
Robicheaux: AIMv2 Avoids Performance Saturation as Scale Increases
“And we can see that this is finally a model that doesn't saturate. It's even at the highest parameter count, it's, it appears to be Well, at the highest parameter account, it appears to be improving in performance with more and more samples seen”
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“Essentially, I think it's kind of been shown now that convolution models, like, just don't benefit from pre-training and just don't, like, have the level of intelligence to transform models.”
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“Models that have been initialized with Clip as their vision encoder, they don't have fine-grained details and the features extracted using Clip because Clip sort of doesn't need to find these fine grade details to do its job correctly, which is just to match c…”
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AssertionSupported
Robicheaux: Adding DINOv2 features degrades multimodal language modeling performance
“As you increase the number of Dynav two features, your model does worse and worse and worse on the actual language modeling task, and that's because Dynav two features were trained completely from a self-supervised manner and completely in image space. It know…”
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AssertionSupported
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“One of my critiques, I guess, of polygeoma one, at least, is that you find that performance saturates as a pre-trained model after only three hundred million examples seen.”
Peter RobicheauxDec 22, 2024▶ 32:52Best of 2024 in Vision [LS Live @ NeurIPS]
AssertionSupported
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“They get 60%, 60% map on Cocoa, which is, like, approaching state of the art, and they train with... You're good. And they train with a much more much more efficiently.”
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