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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 Aufrufe • vor 1 Jahr •via X (Twitter)

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Sumanthvor 1 Jahr

Github Repo:

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Sumanthvor 1 Jahr

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!

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Page to Pixel Publishingvor 1 Jahr

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!

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Rethynk AIvor 1 Jahr

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.

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Sumanthvor 1 Jahr

Absolutely!

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anarki🌟vor 1 Jahr

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

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Pérry Odé 🇪🇺🇩🇪🇺🇦🇮🇱vor 1 Jahr

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

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Machine Learning Community ⭐️vor 1 Jahr

Impressive!

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Sumanthvor 1 Jahr

Indeed!

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Appy Pievor 1 Jahr

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!

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taramavor 1 Jahr

@BenjaminDEKR fyi

Ähnliche Videos

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
0:25

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

124,358 Aufrufe • vor 1 Jahr

SAMURAI gives SAM 2 motion-aware memory! And the results look mind-blowing. This is zero-shot 🤯 Links ⬇️d
0:33

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

Dreaming Tulpa 🥓👑

116,471 Aufrufe • vor 1 Jahr

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.
0:27

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.

Bilawal Sidhu

47,246 Aufrufe • vor 1 Jahr

Segment Anything Model 2 (SAM 2) is a foundation model from Meta FAIR for promptable visual segmentation in images & videos. Available now for anyone to build on for free, open source under an Apache license. Try the demo ➡️
0:35

Segment Anything Model 2 (SAM 2) is a foundation model from Meta FAIR for promptable visual segmentation in images & videos. Available now for anyone to build on for free, open source under an Apache license. Try the demo ➡️

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Steve Krouse

240,119 Aufrufe • vor 1 Jahr

✨ CVPR 2025 highlight: A Distractor-Aware Memory for Visual Object Tracking with SAM2 the authors propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness 🏡 (1/n)🧵👇
0:34

✨ CVPR 2025 highlight: A Distractor-Aware Memory for Visual Object Tracking with SAM2 the authors propose a new distractor-aware memory model for SAM2 and an introspection-based update strategy that jointly addresses the segmentation accuracy as well as tracking robustness 🏡 (1/n)🧵👇

GeekyRakshit (e/mad)

32,669 Aufrufe • vor 1 Jahr

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️
0:51

The Segment Anything Model (SAM) by Meta AI is a step toward the first foundation model for image segmentation. SAM is capable of one-click segmentation of any object from photos or videos + zero-shot transfer to other segmentation tasks. Try the demo ➡️

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186,324 Aufrufe • vor 3 Jahren

The last stage of Segment-Anything-Model-2 (SAM-2) is filled with what look to be dead GELUs. It's a graveyard out there.
0:31

The last stage of Segment-Anything-Model-2 (SAM-2) is filled with what look to be dead GELUs. It's a graveyard out there.

Rudy Gilman

26,564 Aufrufe • vor 1 Jahr

Introducing VT Tensiontrack a cylindrical tracking scenario with a mix of fast, short & long strafes. Uniquely made on @Aimlab with an indicator that informs the player when to increase tension or loosen up. Great for practicing tension control! ✏️ @Lowgravity56 | 🎯 VT Matty
0:46

Introducing VT Tensiontrack a cylindrical tracking scenario with a mix of fast, short & long strafes. Uniquely made on @Aimlab with an indicator that informs the player when to increase tension or loosen up. Great for practicing tension control! ✏️ @Lowgravity56 | 🎯 VT Matty

Voltaic

27,028 Aufrufe • vor 2 Jahren

Things are moving fast! We’re headed for tribulation
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Things are moving fast! We’re headed for tribulation

ittybittytara

23,559 Aufrufe • vor 1 Jahr

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You know that things are moving far too fast when even the 1990s feel as if they're a lifetime ago.
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Lots of things are happening. We are moving pretty fast.
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I spent most of today preparing for CVPR 2024 "Matching Anything by Segmenting Anything" particularly caught my attention. Here are the fast open-vocabulary tracking examples (MASA + YOLO-World). link: ↓ read more
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I spent most of today preparing for CVPR 2024 "Matching Anything by Segmenting Anything" particularly caught my attention. Here are the fast open-vocabulary tracking examples (MASA + YOLO-World). link: ↓ read more

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