Nikhila Ravi (Research Engineering Lead at Meta Superintelligence) explains the training distribution and architectural decoupling of concept recognition and localization in SAM 3.
“We have about 70, more than 70% of the annotations are these like negative phrases that are not present in the image.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
More from Nikhila Ravi
Insight
Ravi: Detection and tracking must decouple due to conflicting representation needs
“The detector needs to be identity agnostic. So if you have a concept dog, it needs to be able to find all instances of that dog. And it needs to sort of have this representation of dog that is the same for all dogs. But when you're tracking those dogs through …”
Nikhila RaviDec 18, 2025▶ 25:31SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
AssertionSupported
Ravi: SAM 3 matches or beats single-task vision SOTA models
“We are really having a unified model that can do many different tasks in the same unified architecture. And so, you know, then the same way that LLMs can do many different tasks without needing a task-specific model. Like with SAM-III, we're able to do image-p…”
Nikhila RaviDec 18, 2025▶ 51:16SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
Insight
Public demos acting as internal annotation tools directly accelerate model quality
“And the other piece here is that the demo is actually the annotation tool. So we actually Use the demo as a way to improve our annotation tool. And so then it becomes very natural to invest in building a good demo because it speeds up your annotation and impro…”
Nikhila RaviAug 7, 2024▶ 18:24Segment Anything 2: Memory + Vision = Object Permanence — with Nikhila Ravi and Joseph Nelson
Disclosure
Meta limits SAM releases to step-change breakthroughs in narrow capabilities
“So as you've probably seen with SAM and SAM-II, it's a fairly narrow problem, but we really try to make it a step change in the capability. And so with each Version. We are trying to limit the focus on one thing that we can know we can do really well. And in t…”
Nikhila RaviAug 7, 2024▶ 30:20Segment Anything 2: Memory + Vision = Object Permanence — with Nikhila Ravi and Joseph Nelson
AssertionSupported
Ravi: SAM 3 concept prompting eliminates manual per-instance clicking
“Essentially, idea of a concept prompt opens up the ability to find all instances of an object category without having to manually click on every single instance, as you would have had to do if you were using SAM-II or SAM-I.”
Nikhila RaviDec 18, 2025▶ 6:21SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
AssertionSupported
Ravi: Meta's SA-Co Benchmark Has Over 200,000 Unique Concepts
“If you look at the size of these benchmarks, the previous benchmark, Peng Chuan mentioned, Elvis, that everyone uses, it has about 1.2 K unique concepts and the benchmark that we created, which we're calling segment anything with concepts or Seiko, COCO for sh…”
Nikhila RaviDec 18, 2025▶ 12:13SAM 3: The Eyes for AI — Nikhila & Pengchuan (Meta Superintelligence), ft. Joseph Nelson (Roboflow)
Made with StarZero
Turn any episode into a week of clips.
This entire site, over 200 episodes transcribed, diarized, checked and made playable,
runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the
moments worth sharing, cuts them, captions them, and reframes them for every feed.
We use essential cookies to make the site work. With your permission we
also use analytics cookies (Google Analytics and Mixpanel) to understand
usage and improve StarZero. See our Cookie Policy.