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MetaAI's SAM 2 struggles when things move fast or when there are crowded, fast-moving objects! Introducing SAMURAI: An adaptation of the Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory. 100% Open Source

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Sumanth

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324,545 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 11

Фото профиля Sumanth
Sumanth1 год назад

Github Repo:

Фото профиля Sumanth
Sumanth1 год назад

If you find this useful, RT to share it with your friends. Don't forget to follow me @Sumanth_077 for more such content and tutorials on Python, AI & ML!

Фото профиля Page to Pixel Publishing
Page to Pixel Publishing1 год назад

The Art of Flight is a homage to 80s/90s arcade action shmups with a fresh twist on the genre. Pilot multiple ships at the same time to take on oncoming waves of enemies in this fast paced space shooter. Wishlist on Steam today!

Фото профиля Rethynk AI
Rethynk AI1 год назад

That’s a brilliant upgrade! SAMURAI sounds like a game-changer for dynamic environments where traditional SAM models fall short. Motion-aware memory could make zero-shot visual tracking far more robust, especially in real-world applications like sports analysis or autonomous vehicles.

Фото профиля Sumanth
Sumanth1 год назад

Absolutely!

Фото профиля anarki🌟
anarki🌟1 год назад

instant @arXivBangers haha let’s gooooo!!!!!

Фото профиля Pérry Odé 🇪🇺🇩🇪🇺🇦🇮🇱
Pérry Odé 🇪🇺🇩🇪🇺🇦🇮🇱1 год назад

If I were a producer of self-shooting and AI-driven drones, SAMURAI would be preferable in this case. 👍

Фото профиля Machine Learning Community ⭐️
Machine Learning Community ⭐️1 год назад

Impressive!

Фото профиля Sumanth
Sumanth1 год назад

Indeed!

Фото профиля Appy Pie
Appy Pie1 год назад

MetaAI's SAM 2 meets its match with fast motion and crowded scenes, but SAMURAI steps in! Motion-aware memory and zero-shot visual tracking make it a game-changer. Plus, it's 100% open source!

Фото профиля tarama
tarama1 год назад

@BenjaminDEKR fyi

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SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware check out this SAM2 vs SAMURAI comparison! - paper: - code: - license: Apache-2.0
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SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware check out this SAM2 vs SAMURAI comparison! - paper: - code: - license: Apache-2.0

SkalskiP

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SAMURAI gives SAM 2 motion-aware memory! And the results look mind-blowing. This is zero-shot 🤯 Links ⬇️d
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SAMURAI gives SAM 2 motion-aware memory! And the results look mind-blowing. This is zero-shot 🤯 Links ⬇️d

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Just read a neat AI paper called SAMURAI -- it takes SAM 2 (Meta's "segment anything" model) and makes it way better at tracking objects in videos. Basic problem is SAM 2 gets confused when things move fast or there's a crowd of similar objects (big problem for VFX and video intelligence alike). The fix? They basically gave it a sense of motion and smarter memory selection using our old friend the Kalman filter -- helps it remember where things were going instead of just where they are. No retraining needed and runs in realtime. Elegant solution that matches (and even beats!) a lot of purpose built tracking systems. Nice example of how sometimes you don't need fancy new architectures -- just smart ways to use what the model already knows, plus some tried & tested classical methods. Every time someone's like "we solved tracking!" I check the methods section and... yep, there's our boy Kalman, still crushing it after all these years. Sometimes the old school stuff just works.
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Just read a neat AI paper called SAMURAI -- it takes SAM 2 (Meta's "segment anything" model) and makes it way better at tracking objects in videos. Basic problem is SAM 2 gets confused when things move fast or there's a crowd of similar objects (big problem for VFX and video intelligence alike). The fix? They basically gave it a sense of motion and smarter memory selection using our old friend the Kalman filter -- helps it remember where things were going instead of just where they are. No retraining needed and runs in realtime. Elegant solution that matches (and even beats!) a lot of purpose built tracking systems. Nice example of how sometimes you don't need fancy new architectures -- just smart ways to use what the model already knows, plus some tried & tested classical methods. Every time someone's like "we solved tracking!" I check the methods section and... yep, there's our boy Kalman, still crushing it after all these years. Sometimes the old school stuff just works.

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