Interesting problem and a good write up! This reminds me a little of audio processing where there are 2 representations, time domain and frequency domain. I’m n

Interesting problem and a good write up! This reminds me a little of audio processing where there are 2 representations, time domain and frequency domain. I’m no expert at this but my understanding is if you want to search for “when” some chunk of audio happened, you first need to convert to the frequency domain via Fourier transfer. But then you lose the time info. So you can’t just take the Fourier transform of the whole file, or even 10 second chunks… you have to take a bunch of short overlapping Fourier transforms – overlapping so you get the nearby context, and short so that you have a higher resolution idea of when something occurred. I wonder if a similar idea would work here, where you could search at various “zoom levels” - first search for an entire video that’s nearby in terms of embedding, then search within 50%-overlapped 60-second chunks, then within 50%-overlapped 1-second chunks.