> Running CLIP even on 27 million frames (1 day of footage) is super expensive. We've built some infra that makes processing video efficient (forms of parallelization + filtering), without you having to think about it.

With your massive parallel infra you're still processing 27 millions frames, right?

> If you're looking at some generic things that are similar to what CLIP was trained on, this would work. Say you're interested in specific physical security metrics, or monitoring defective parts, or specific things about traffic, etc. CLIP might just say "people walking" or "car in intersection" or "part on conveyer belt" which isn't meaningful enough if all your images are exactly that, but with other small differences.

That's exactly transfer learning is for. I am saying to do it with because you automatically get a pretrained model which can do much more than that. Imagine doing a search "a person wearing red cap and yellow hand bag walking toward the exit" or "A person wearing a shirt with mark written on it". Can your system do it right now?