What is a frame embedding?
A frame embedding treats one frame of video as an image and embeds it. It is the finest-grained temporal representation of video content: every sampled instant becomes its own searchable point. Sampling every frame is usually unnecessary, so frames are typically embedded at a fixed rate or at detected key moments.
Frame embeddings capture appearance at an instant but not motion, which is what separates them from clip and segment embeddings.
Key takeaways
- A frame embedding represents a single instant of video.
- It enables image-style search inside video content.
- It captures appearance but not motion or behavior.
How it works
Frames are sampled from the video, each passed through an image embedding model, and the vectors indexed with timestamps. Searching with an image or text query then returns exact moments across an entire video corpus. Aggregating consecutive frame embeddings, by pooling or a temporal model, produces clip-level or video-level representations.
Why it matters
Video is dense and expensive to review, and frame embeddings give it the same searchability images enjoy: find every moment that looks like this across hours of footage. They are also the practical starting point for video curation pipelines, with segment and video embeddings layered on top when motion matters.
Frequently asked questions
How often should frames be sampled for embedding?
Common practice is one to a few frames per second, balancing index size against temporal resolution. Key-frame detection can focus sampling on visually distinct moments.
When are frame embeddings not enough?
When the thing you want to find is a behavior or motion pattern, which single frames cannot capture. Segment or video embeddings handle those.
Related terms
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