Peter Robicheaux of Roboflow explains why multimodal foundation models suffer from perceptual blindness on fine visual details.
Assertion Supported
Robicheaux: 2B PaliGemma 2 Beats ChatGPT on MMVP with 47.3%
“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 …”
Assertion Supported
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”
Opinion
Robicheaux: Convolutional models fail to benefit from pre-training compared to transformers
“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.”
Assertion Supported
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…”
Assertion Supported
Robicheaux: PaliGemma 1 Pre-Training Saturates at 300M Examples
“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.”
Assertion Supported
Robicheaux: Florence-2 Achieves 60% mAP on COCO
“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.”