For video AI, setup can matter more than the model
VideoDB evaluated on real video tasks, including retrieval, , summarization, and metadata . The biggest limit was often not the model itself, but how the video task was configured. worked best when it followed scene changes, not just a fixed number of frames per second.
Evenly spaced frames can miss important events, while too many frames can fill the context with repeated information. The strongest setup sampled more around scene boundaries and less during stable parts of the video. Prompt structure also changed results a lot.
A broad request to describe a video gave broad answers, while asking for each separate activity with start and end times produced more useful output. Standard benchmark scores did not predict real work well; near-miss negatives were more useful because they showed where a weaker setup would appear to pass but actually fail.
Key points
- Real video tasks included retrieval, , summarization, and metadata .
- based on scene changes beat simple fixed-rate sampling.
- Too many similar frames can waste context and raise cost.
- Specific prompts produced more useful answers than broad video descriptions.
- Near-miss negatives were better than standard benchmark scores for judging real .