Meta AI recently introduce a foundational model for image segmentation called Segment Anything Model or SAM in short. This video explain the motivation for SAM, the model architecture, its novel SA1B dataset and the results and limitations of zeroshot transfer learning.

Hope its useful.

segment anything website: https://segmentanything.com
segment anything code: https://github.com/facebookresearch/s...
segment anything demo: https://segmentanything.com/demo
SA1B dataset:   / segmentanythingdownloads  


⌚ ⌚ ⌚ TIMESTAMPS ⌚ ⌚ ⌚
0:00 Foundational models in NLP
1:07 Problem with Vision Models
2:01 Prompting in Segmentation
2: 56 SAM Model Architecture
5:15 SAM Model Animation
6:15 SAM Model Training (Data Engine)
9:21 SA1B Dataset
10:04 ZeroShot Transfer Learning Tasks
11:33 ZeroShot Transfer Learning Results


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Multiple View Geometry in Computer Vision by R Hartley and A Zisserman https://amzn.to/3XKVOWi

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I am a Machine Learning Researcher / Practioner who has seen the grind of academia and startups equally. I started my career as a software engineer 15 years back. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution just started. Life has changed for the better ever since.

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