Segment Anything Model (SAM) from Meta AI: model architecture, data engine, results and limitations

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 SA-1B dataset and the results and limitations of zero-shot transfer learning.

Hope its useful.

⌚️ ⌚️ ⌚️ 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 - SA-1B Dataset
10:04 - Zero-Shot Transfer Learning Tasks
11:33 - Zero-Shot Transfer Learning Results

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🛠 🛠 🛠 MY SOFTWARE TOOLS 🛠 🛠 🛠

📚 📚 📚 BOOKS I HAVE READ, REFER AND RECOMMEND 📚 📚 📚

MY KEY LINKS

WHO AM I?
I am a Machine Learning Researcher / Practioner who has seen the grind of academia and start-ups 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.

#machinelearning #deeplearning #aibites

[vlogsofanundergrad]

Great job mate, keep going. Could you please update on current research for proof-of-concept CLIP/text based prompting for SAM?
Note one suggestion kindly: Don't make the text animation bounce. It's very distracting when trying to read the text. Maybe you can try other kind of animation or just keep it simple.
Even other kinds of objects in flowchart bounce when they appear. Please avoid this bounce animation.

[ewjloor]

Great video! Thanks for explaining many papers. Can you stop with the music in the background, it’s a bit distracting 😝

[wklusmy]

thanks, very nice. can we your slides?