Joseph Nelson
speaker
239 appearances
1 recordings
1 series
first heard Dec 2025
last heard 18 Dec
Joseph Nelson’s voice in public audio — every appearance, attributed to the second.
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Yeah, so each so for example, like each dog that appears in the video, each one of those needs to be tracked independently.
There was something else that you started to allude to in the paper that I was hoping we would spend some time discussing, and it's the interaction of SAM3 and LLMs, Llama and others.
So using SAM3 to almost be like a tool call for LLMs to give them better grounding and give them better visual understanding.
And there's a a paper in the table where you describe and
increase in in performance.
It's kind of alluding, I think, to maybe where things are going for using SAM three as a component part of
multimodal architectures.
Do you want to describe a bit about what the introduction of that work was meaning to showcase and uh how the interaction of SAM three and LLMs is envisioned to be important?
Go to table eight in the paper as
Got it.
I can show an example here that might be insightful too.
Go for it.
So even comparing even comparing like Sam three and Gemini, let's say that we just want to have them do like a an optic detection task here of finding here we're gonna prompt with a speedometer.
and RPMs and we're going to ask for things like indicator light number and needle.
And if we run SAM3 head to head with Gemini 3 and Florence 2, almost as a baseline of like where things have been, and we see each of the results, first things first, you'll note that the speed of inference of SAM3, uh
is quite quick.
Um this is just calling the Gemini three pro API.
So whatever is provided from hosted compute is sort of what you get on the on the response time.
And then the second thing you'll note is in addition to speed is some of the accuracy of of results.
Who might get it we might have a timeout error.
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