Multi-Image and Video Frames

Sequences

Part of: Multimodal AI: Vision & Images

A model cannot watch a video. It has no eyes and no clock. So how do products "analyze a clip"? They cheat: they pull out still frames at intervals, line them up like a flipbook, and feed that handful of images into the same vision request you already know. A two-minute video becomes maybe twenty pictures. Once you see that trick, "video understanding" stops being a different feature and becomes multi-image prompting with a sampling step in front. What it is Multi-image prompting is putting more than one image in a single request so the model can compare, sequence, or reason across them. Video frame sampling is the special case where those images are stills pulled from a video at a chosen rate, one frame per second, every tenth frame, or however dense you need. The model treats the frames as an ordered list of pictures, not as motion. It infers what changed between them the way you would by flipping through photos, not by watching playback. How it works The content list simply holds several image blocks, often interleaved with text that labels them: Two numbers govern everything: 1. Sampling rate. How many frames you pull. Sparse sampling is cheap but can miss fast events; dense sa

Challenge: The Frame Sampler