🚀 Meta’s SAM2 vs. SAMurai: Advancing Object Tracking in AI! 🤖 🔍 SAM2: A breakthrough in segmentation, but struggles with overlaps, losing track of objects in tricky scenarios. 🌟 SAMurai: The new contender that takes object tracking to the next level—mastering obscurities and keeping a lock on objects even when the view gets tricky. 🔥 Cutting-edge research reshaping how AI sees and understands the world. Watch to explore the gaps and innovations in this fascinating space! #AI #Meta #SAM2 #SAMurai #ObjectTracking #Segmentation #Research #AIInnovation #TechExplained #ComputerVision

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TikTok 1:10 Oct 01, 2025

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🚀 Meta’s SAM2 vs. SAMurai: Advancing Object Tracking in AI! 🤖 🔍 SAM2: A breakthrough in segmentation, but struggles with overlaps, losing track of objects in tricky scenarios. 🌟 SAMurai: The new contender that takes object tracking to the next level—mastering obscurities and keeping a lock on objects even when the view gets tricky. 🔥 Cutting-edge research reshaping how AI sees and understands the world. Watch to explore the gaps and innovations in this fascinating space! #AI #Meta #SAM2 #SAMurai #ObjectTracking #Segmentation #Research #AIInnovation #TechExplained #ComputerVision

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I'm really excited about some of the research that labs such as Meta are publishing in the area of computer vision. They have been pushing out steadily this segment anything series of models now called SAM2, which effectively allows you to select any object in a frame or a series of frames such as a video. And it basically draws a mask around this object and understands all objects similar to that in the series of frames. So it's quite powerful when you're building any kind of like video understanding systems. It does, however, present an interesting challenge that this research from the University of Washington has highlighted, which is when you draw a bounding box or a mask around an object and that object then becomes obscured, the SAM2 model actually loses the understanding of where that object is. So as you can see right there, we've lost the object when there was an overlap. And this research straight out of University of Washington called Samurai is actually capable of understanding and tracking the object despite the overlap or the loss of the object taking place in the frame. So it's quite interesting and it's open source, Apache too, so you can build something on top of it yourself. We're using it to improve our video search systems over at MixBeak and we're really excited about this series of research. What are you building with SAM2 or SAM segment anything models? Let me know.

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