Hey HN! I'm very proud to release the deepest interview deep dive into the SAM model I can find on the Internet (seriously - i looked on youtube and listennotes and all of them were pretty superficial). the Roboflow team has spent the past week hacking on and building with SAM and I ran into Joseph Nelson this weekend and realized he might be the perfect non-Meta-AI person to discuss what it means for developers building with SAM.
so.. enjoy! worked really hard on the prep and editing, any feedback and suggestions/recommendations welcome. still new to AI and new to the podcast game.
It's easy to have someone lay down 30 points for a simple banana shaped outline and compare segmentation to that, but how does this compare to other automatic techniques like spectral matting (which is now 16 years old) ?
Deep methods are a vast improvement over classical computer vision techniques. Classical techniques can be thought of as a function of the raw pixel data. Deep learning techniques understands the context and are more likely to get segment how a human might segment.
spectral matting, as I understand it, is used for subject/foreground and background separation.
It's easy to say something is better, but good computer graphics and computer vision papers compare themselves to the state of the art.
This is ignoring all that and comparing itself to the most manual method possible. This also happens sometimes and is more of a marketing stunt to people who don't follow current research.
any requests for under-covered topics? i felt like this one resonated because somehow the other podcasters/youtubers seemed to miss how big of a deal it was. hungry for more.
so.. enjoy! worked really hard on the prep and editing, any feedback and suggestions/recommendations welcome. still new to AI and new to the podcast game.
edit: Video demo is here in case people miss it https://youtu.be/SZQSF-A-WkA