DeepSeek interpreting screenshots and images I send it at fractions of what I pay Claude and ChatGPT, for me, is of far higher priority than supporting dictation. There are workarounds for dictation but not image processing.
Indeed, Gemini really is incredible at image analysis. Yesterday I pointed it at some sloppy handwritten notes and asked it to add up the numbers in the right column, and it did it no problem. I've also used it to find out what TV show or actor is on screen, and various other things. It's quite impressive.
> Indeed, Gemini really is incredible at image analysis. Yesterday I pointed it at some sloppy handwritten notes and asked it to add up the numbers in the right column, and it did it no problem. I've also used it to find out what TV show or actor is on screen, and various other things. It's quite impressive.
I do not know if it works as well as Gemini, but Salesforce (of all places) has a model that does something similar.
What's "neat" about the Salesforce one is that you can run it locally and just iterate it over as many images as you feel like.
For instance, it should be possible to take a movie, pull a hundred images out of the h265 file, have the salesforce model evaluate what is happening at that moment in the movie, and then use that to create an index.
That's just ONE use for it, and I can think of dozens.
On a 5090 it was able to generate text descriptions of a folder full of approximately 500 images in under a minute. (Anecdotal evidence, admittedly.)
I got a shirt I liked from a conference, and I didn't know who made it. It was soft, fit comfortably... I took a picture of some random numbers on a tag and Gemini parsed out the numbers and found the manufacturer. Pretty neat
I don't know what runs on my phone's Google Translate app, but whatever it is, they are doing an insult to their models by it being so bad. It's amazing at picking up sound if spoken directly into the unit, but if trying to hold any kind of conversation or listen to anything even a little bit far away, it falls completely apart, is good for basically nothing.
This is obviously different than the models most people are discussing here, which are much bigger. But it's damaging the Gemini brand in general, by association, if nothing else.
CNNs are not SoTA anymore when it comes to large models, and also are not used to provide interpretations of images as text, but rather to classify, do semantic segmentation, etc.
CNNs are fine when trained with a good recipe. There are very few good studies comparing them with proper hyperparam search and all the training tricks applied consistently. Transformers are good but ViT vs CNN is not some settled issue. Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great.
I agree, but since we're talking about imagine understanding with text output, clearly a CNN is unsuitable. My previous comment was overly reductive and CNNs can still be SoTA depending on your performance metrics. I spent the earlier part of my career training CNNs, and they are very pleasant to work with.
>Transformers are more hyped and more popular with the tech enthusiasts who just read forums and news, but if you need stuff done, CNNs are still great.
Vits are straight up more popular for ML research now, it's not just 'tech enthusiasts'.
CNNs excel in vision tasks where you have limited compute, limited memory, limited data, and want something that works super well and quick. People usually don't hook CNNs up to a transformer to get language understanding either, you have to train bespoke CNNs for specific tasks
ViTs excel where you're unbounded in compute + data and also want text understanding or have a conversation about an image
These are vibes. ViT has been shown to work fine on small data with proper hyperparam and most of what you mention is actually doable just fine with the other architecture as well.