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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

Sumanth

77,367 subscribers

324,662 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 Publishing2 лет 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

Похожие видео

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

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

124,472 просмотров • 1 год назад

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.

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

47,291 просмотров • 1 год назад

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 ➡️

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 ➡️

AI at Meta

97,811 просмотров • 2 лет назад

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

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

27,101 просмотров • 2 лет назад

🚨Fast Moving Objects Captured With Infrared Camera Nov. 24, 2025 around 10pm PST - Los Angeles, CA Just found this interesting capture. The OP states: "3 objects captured with an infrared camera in the night sky above Los Angeles. They were moving fast across the sky, much faster than typical airliners I see. These objects did not appear on my flight tracker app. There are two parts of this clip, both played back in real time; one that shows the original camera perspective, the second part of clip is zoomed in and stabilized." #ufotwitter #uapX Source:

🚨Fast Moving Objects Captured With Infrared Camera Nov. 24, 2025 around 10pm PST - Los Angeles, CA Just found this interesting capture. The OP states: "3 objects captured with an infrared camera in the night sky above Los Angeles. They were moving fast across the sky, much faster than typical airliners I see. These objects did not appear on my flight tracker app. There are two parts of this clip, both played back in real time; one that shows the original camera perspective, the second part of clip is zoomed in and stabilized." #ufotwitter #uapX Source:

Skywatch Signal

53,070 просмотров • 9 месяцев назад

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,438 просмотров • 10 месяцев назад

Huge drop from Tencent! AuK - turns speech gen/editing into a text-prompt playground. You can control literally anything with natural language. - zero-shot voice cloning, no reference transcript - swap an accent, inject anger, fix background noise, or change a single spoken word in a track. - full TTS, vocal separation - studio-grade cleanup The scale behind this is wild: trained on ~2 million hours of data. - Ridiculously low error rates - 2.65% uses Qwen3-Omni/ASR/ForcedAligner. also AuK-Flash for lightning-fast inference. for dubbing, podcasting, game audio... weights are fully open-source!

Huge drop from Tencent! AuK - turns speech gen/editing into a text-prompt playground. You can control literally anything with natural language. - zero-shot voice cloning, no reference transcript - swap an accent, inject anger, fix background noise, or change a single spoken word in a track. - full TTS, vocal separation - studio-grade cleanup The scale behind this is wild: trained on ~2 million hours of data. - Ridiculously low error rates - 2.65% uses Qwen3-Omni/ASR/ForcedAligner. also AuK-Flash for lightning-fast inference. for dubbing, podcasting, game audio... weights are fully open-source!

Wildminder

17,510 просмотров • 25 дней назад

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THAT MOMENT WHEN NVIDIA CEO JENSEN HUANG WAS ASKED ABOUT HIS COMMENTS ON CHINA BEATING THE U.S. IN THE AI RACE: “THAT’S NOT WHAT I SAID. WHAT I SAID WAS THAT CHINA HAS VERY GOOD AI TECHNOLOGY. THEY HAVE MANY AI RESEARCHERS. IN FACT, 50% OF THE WORLD’S AI RESEARCHERS ARE IN CHINA. THEY DEVELOP VERY GOOD AI TECHNOLOGY. THE MOST POPULAR OPEN-SOURCE AI MODELS IN THE WORLD TODAY ARE FROM CHINA. SO THEY ARE MOVING VERY, VERY FAST. THE UNITED STATES HAS TO CONTINUE TO MOVE INCREDIBLY FAST. OTHERWISE, THE WORLD IS VERY COMPETITIVE, SO WE HAVE TO RUN FAST.”

Vivek Sen

155,599 просмотров • 4 месяцев назад

🇺🇸VIVEK: TRUMP WORKS FAST—HOW IS THAT A BAD THING? "He definitely is moving at a faster pace than probably any president has in modern history or in our lifetimes. The fact that the best allegation [Democrats] are coming up with is that 'the pace is just too crazy' when you had open borders and a flailing economy. I do think that Fast and Furious is exactly what we need." Source: Vivek Ramaswamy, Shawn Ryan Show

🇺🇸VIVEK: TRUMP WORKS FAST—HOW IS THAT A BAD THING? "He definitely is moving at a faster pace than probably any president has in modern history or in our lifetimes. The fact that the best allegation [Democrats] are coming up with is that 'the pace is just too crazy' when you had open borders and a flailing economy. I do think that Fast and Furious is exactly what we need." Source: Vivek Ramaswamy, Shawn Ryan Show

Mario Nawfal

604,793 просмотров • 1 год назад

Meta released SAM3 a model to reconstruct objects from segmented images. I tried it and here are 4 things I found out - The generation is incredibly fast when compared with other 3D GenAI models - The models generated are not meshes but Gaussian Splatting. I was surprised that in the paper it was not mentioned anywhere - When generating multiple objects the arrangement is a bit messed up. - Unfortunately, it is impossible to download any of the .ply generated 😔 I cannot stop thinking about the implications of this in XR. Objects in the real world could be almost instantly turned into photorealistic copies and even rearranged to the user's wish.

Meta released SAM3 a model to reconstruct objects from segmented images. I tried it and here are 4 things I found out - The generation is incredibly fast when compared with other 3D GenAI models - The models generated are not meshes but Gaussian Splatting. I was surprised that in the paper it was not mentioned anywhere - When generating multiple objects the arrangement is a bit messed up. - Unfortunately, it is impossible to download any of the .ply generated 😔 I cannot stop thinking about the implications of this in XR. Objects in the real world could be almost instantly turned into photorealistic copies and even rearranged to the user's wish.

Gabriele Romagnoli

32,447 просмотров • 10 месяцев назад

CAPTCHA IS OFFICIALLY OUTDATED A new open-source library called Cap is changing how websites stop bots. No puzzles. No traffic lights. No “select all bikes” anymore. Instead, it uses a SHA-256 proof-of-work system — simple, silent, and fast. Why devs are switching: • Only ~20KB in size • Zero tracking, zero data collection • No images, no user friction • Works with any JS runtime • Fully customizable (visible, invisible, floating modes) • Zero dependencies • Can be deployed instantly via Docker This is a full replacement for traditional CAPTCHA systems. Cleaner UX. Faster websites. Better privacy. 100% open-source on GitHub Link in comments.

CAPTCHA IS OFFICIALLY OUTDATED A new open-source library called Cap is changing how websites stop bots. No puzzles. No traffic lights. No “select all bikes” anymore. Instead, it uses a SHA-256 proof-of-work system — simple, silent, and fast. Why devs are switching: • Only ~20KB in size • Zero tracking, zero data collection • No images, no user friction • Works with any JS runtime • Fully customizable (visible, invisible, floating modes) • Zero dependencies • Can be deployed instantly via Docker This is a full replacement for traditional CAPTCHA systems. Cleaner UX. Faster websites. Better privacy. 100% open-source on GitHub Link in comments.

divyansh tiwari

113,662 просмотров • 5 месяцев назад

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

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Boyi Li

67,643 просмотров • 9 месяцев назад

Is There a 70-Year Plan Behind the Global Crisis? There are warnings of a global famine. Not someday. But something being set in motion right now. Key trade routes are under pressure. The Strait of Hormuz — one of the most critical pathways for global energy and supply. If that gets disrupted… it doesn’t just affect oil. It affects food. Supply chains. Entire nations. And when systems like this start breaking down… the consequences move fast. So the real question is: Is this a crisis… or something being engineered? Alex Newman

Is There a 70-Year Plan Behind the Global Crisis? There are warnings of a global famine. Not someday. But something being set in motion right now. Key trade routes are under pressure. The Strait of Hormuz — one of the most critical pathways for global energy and supply. If that gets disrupted… it doesn’t just affect oil. It affects food. Supply chains. Entire nations. And when systems like this start breaking down… the consequences move fast. So the real question is: Is this a crisis… or something being engineered? Alex Newman

Sarah Westall

471,584 просмотров • 6 месяцев назад

1. Smear frames replicate real life motion blur that happens in cameras and our eyes. So be careful to only use them when a movement is too fast to follow. That usually means an object is moving a large distance in 1 to 2 frames. A basic rule is that the audience should have trouble telling if you used a smear or not. (But don’t worry they will feel it in the smoothness of motion) 2. Old looney tunes cartoons are great to reference when practicing smears as those animators really pushed the limits of what could work :) 3. Anticipation and follow through are the secret to making smears effective. They allow audiences to make sense of what happened during a quick motion.

1. Smear frames replicate real life motion blur that happens in cameras and our eyes. So be careful to only use them when a movement is too fast to follow. That usually means an object is moving a large distance in 1 to 2 frames. A basic rule is that the audience should have trouble telling if you used a smear or not. (But don’t worry they will feel it in the smoothness of motion) 2. Old looney tunes cartoons are great to reference when practicing smears as those animators really pushed the limits of what could work :) 3. Anticipation and follow through are the secret to making smears effective. They allow audiences to make sense of what happened during a quick motion.

Hater

67,659 просмотров • 5 месяцев назад

Why do time slow down as you approach the speed of light? Because space and time are not separate things. They are woven together into spacetime, and the speed of light is built into its structure. Every object moves through spacetime at the same fundamental rate. When you are at rest, almost all of that motion is through time. As you move faster through space, some of that motion is redirected away from time. The total stays constant, so your clock must tick more slowly. This is not philosophy or metaphor. It has been measured with atomic clocks on airplanes and satellites. Move fast enough, and you do not just cross distances differently, you experience less time itself.

Why do time slow down as you approach the speed of light? Because space and time are not separate things. They are woven together into spacetime, and the speed of light is built into its structure. Every object moves through spacetime at the same fundamental rate. When you are at rest, almost all of that motion is through time. As you move faster through space, some of that motion is redirected away from time. The total stays constant, so your clock must tick more slowly. This is not philosophy or metaphor. It has been measured with atomic clocks on airplanes and satellites. Move fast enough, and you do not just cross distances differently, you experience less time itself.

The Scientific Lens

12,149 просмотров • 7 месяцев назад

remind-reid-tracker REMIND — RE-Identification with Memory for INDoor Navigation REMIND addresses a core challenge in visual tracking: re-identifying objects that disappear and reappear, look similar to one another, or are observed from changing viewpoints. Rather than relying on position or motion cues, REMIND builds appearance-based identity models per object using DINOv3 patch features, decomposed into: Global and part-level descriptors — K-means and attention-guided semantic parts extracted per detection Relational context (neighbor sets) — structural scene layout encoded as co-occurrence graphs of neighboring objects Known-set distance disambiguation — geometry-aware resolution of visually ambiguous groups Adaptive memory — per-object appearance, part, and background models that update over time The association pipeline runs a per-frame sequence of visual evidence building, context activation, global Hungarian assignment, and post-assignment guards, producing explicit uncertainty signals (ambiguous, provisional) alongside confident identity decisions. Evaluation outputs span case, object, frame, class, scene, and batch levels, with full internal telemetry for diagnostic analysis.

remind-reid-tracker REMIND — RE-Identification with Memory for INDoor Navigation REMIND addresses a core challenge in visual tracking: re-identifying objects that disappear and reappear, look similar to one another, or are observed from changing viewpoints. Rather than relying on position or motion cues, REMIND builds appearance-based identity models per object using DINOv3 patch features, decomposed into: Global and part-level descriptors — K-means and attention-guided semantic parts extracted per detection Relational context (neighbor sets) — structural scene layout encoded as co-occurrence graphs of neighboring objects Known-set distance disambiguation — geometry-aware resolution of visually ambiguous groups Adaptive memory — per-object appearance, part, and background models that update over time The association pipeline runs a per-frame sequence of visual evidence building, context activation, global Hungarian assignment, and post-assignment guards, producing explicit uncertainty signals (ambiguous, provisional) alongside confident identity decisions. Evaluation outputs span case, object, frame, class, scene, and batch levels, with full internal telemetry for diagnostic analysis.

Ryohei Sasaki@engineer

19,013 просмотров • 2 месяцев назад

NEW EPISODE: “Do I want to do this? There are going to be days when I'm having fun with it, and days when I'm not. In the grand scheme of things, I've memorized a whole lot of stuff that I have no interest in. When I get the outcome, I save whether it's easy or not for later, and I just enjoy the swimming pool of accomplishment.” Anthony Metivier Memory Expert

NEW EPISODE: “Do I want to do this? There are going to be days when I'm having fun with it, and days when I'm not. In the grand scheme of things, I've memorized a whole lot of stuff that I have no interest in. When I get the outcome, I save whether it's easy or not for later, and I just enjoy the swimming pool of accomplishment.” Anthony Metivier Memory Expert

Tetragrammaton

53,873 просмотров • 2 месяцев назад