Unified models enable faster error corrections via refinement clicks
Nikhila Ravi · Segment Anything 2: Memory + Vision = Object Permanence — with Nikhila Ravi and Joseph Nelson · Aug 7, 2024 · at 37:45
Nikhila Ravi, lead author of SAM 2 at Meta FAIR, discusses how unifying image prompting and video mask tracking inside a single model improved video annotation throughput.
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“And we found that, you know, going from each phase, it both improved the efficiency and it improved the data quality. And in particular, when you get rid of this two-part model, one of the advantages is that when you make refinement clicks, so You prompt the model in one frame to select an object. Then you propagate those predictions to all the other frames of the video to track the object. But if the model makes a mistake and you want to correct it, when you have this unified model, you only need to provide refinement clicks. So you can provide maybe a negative click to remove a region or a positive click to add a region. But if you had this decoupled model, you would have to delete That frame prediction and re-annotate from scratch.”
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