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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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Dreaming Tulpa 🥓👑

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116,495 görüntüleme • 1 yıl önce •via X (Twitter)

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Dreaming Tulpa 🥓👑 profil fotoğrafı
Dreaming Tulpa 🥓👑1 yıl önce

Project Page: Code:

Risichad 🦾 profil fotoğrafı
Risichad 🦾1 yıl önce

Drones are heavy breathing !

CS Martin profil fotoğrafı
CS Martin1 yıl önce

Just strap yourselves in... This is going to be a bumpy, mind-blowing ride. Wow. 🤯

0xeik.eth (e/acc) profil fotoğrafı
0xeik.eth (e/acc)1 yıl önce

Target locking just bacame better

Risphere profil fotoğrafı
Risphere1 yıl önce

Crazy!

Cryptico10 ⭕️🟩 profil fotoğrafı
Cryptico10 ⭕️🟩1 yıl önce

A-M-A-Z-I-N-G

Harryson profil fotoğrafı
Harryson1 yıl önce

I don't know how to install it, is there a tutorial for that?

The AI Edge profil fotoğrafı
The AI Edge1 yıl önce

Impressive occlusion detection and seems to handle sudden background changes really well.

Dreaming Tulpa 🥓👑 profil fotoğrafı
Dreaming Tulpa 🥓👑1 yıl önce

Yep, it seems to work exceptionally well

@Be_all profil fotoğrafı
@Be_all1 yıl önce

ccp need this tools 🤗

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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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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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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 vs. MetaAI's SAM 2! Traditional visual object tracking struggles in crowded, fast-moving, or self-occluded scenes, as does SAM2. Meet SAMURAI: a completely open-source adaptation of the Segment Anything Model for zero-shot visual tracking! Here's why it's a game-changer: 🚫 No need for retraining or finetuning 🎯 Boosts success rate and precision 🤖 Motion-aware memory selection 💪 Zero-shot performance on diverse datasets But that's not all: 🔬 Refines mask selection 🔮 Predicts object motion effectively 📈 Gains: 7.1% AUC on LaSOT, 3.5% AO on GOT-10k 🏆 Competes with fully supervised methods without extra training Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights & tutorials on AI and Machine Learning.
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