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DINOv2 by Meta AI is the first method for training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards. More details and a demo ⬇️

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AI at Meta

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96,119 просмотров • 3 лет назад •via X (Twitter)

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This is literally magic 🤯 FMA-Net is a new AI method for video deblurring! It uses complex motion representation learning for spatio-temporally-variant restoration with kernels that are aware of motion trajectories. More info below ⬇️⬇️
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This is literally magic 🤯 FMA-Net is a new AI method for video deblurring! It uses complex motion representation learning for spatio-temporally-variant restoration with kernels that are aware of motion trajectories. More info below ⬇️⬇️

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Can you make a jigsaw puzzle with two different solutions? Or an image that changes appearance when flipped? We can do that, and a lot more, by using diffusion models to generate optical illusions! Continue reading for more illusions and method details 🧵
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many parts of Hugging Face Hub is actually powered by open machine learning models 🥹 translation feature is one of them, it uses a very tiny (600M) multilingual translation model by AI at Meta 💗
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Placing objects sounds simple… until robots have to do it. This method makes it simple, fast & reliable. [Github ⬇️] Robotic object placement is tough, especially with stacking, hanging, or insertion. AnyPlace is a new two-stage method that uses only synthetic data and a vision-language model to teach robots where and how to place objects; even in the real world. Why this works ✅ Finds the right spot with help from vision-language models ✅ Handles stacking, insertion, and hanging with no real-world training ✅ Trained on synthetic data using Blender and IsaacSim ✅ Works in the real world without fine-tuning It shows that smart use of simulation and language models can make robotic placement tasks easier, faster, and more reliable. Github: Paper: Thank you for sharing Animesh Garg !

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Meta released LongVU: a new video LM that can handle long videos (great performance, battle-tested by me ⚔) TLDR; 1️⃣ downsample using DINOv2 to eliminate redundant scenes 🦖 2️⃣ fuse rest of the features using DINOv2 and SigLIP 3️⃣ select some tokens, pass to Qwen2/Llama-3.2-3B

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Today, Nesa is excited to announce cross-chain support with Sei. As part of this new development, Nesa will be featuring a dedicated ramp for Sei’s rich ecosystem of applications to seamlessly integrate with AI. Now any Sei dapp can run AI inference on hundreds of models supported by Nesa, including the largest LLMs and most popular models across Vision, Language, and Generative AI. For developers, we’ll have information soon on how to access AI inference on Nesa via smart contracts from the Sei blockchain. Stay tuned for more details on this ground breaking development for decentralized AI.

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First look at SPARTA, a distributed AI training algorithm that avoids synchronization by randomly exchanging sparse sets of parameters ( 1,000x reduction in inter-GPU communication, enabling training of large models over slow bandwidths without specialized infrastructure. SPARTA works on its own but can also be combined with sync-based low communication training algorithms like DiLoCo for even better performance.
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First look at SPARTA, a distributed AI training algorithm that avoids synchronization by randomly exchanging sparse sets of parameters ( 1,000x reduction in inter-GPU communication, enabling training of large models over slow bandwidths without specialized infrastructure. SPARTA works on its own but can also be combined with sync-based low communication training algorithms like DiLoCo for even better performance.

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Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything
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Are you safer with LIDAR, or are you safer with vision? This is a false dichotomy. The more pertinent question today is "do you have something, or do you have nothing?" As you can see from the clips below, vision based systems avoid countless potential collisions every day. The difference between a crash and no crash isn't what sensor suite you chose — it's whether you have any AI on your car at all. Even if we concede that LIDAR may help prevent some additional crashes, we are really debating whether it is 1% of crashes or 0.00001% of crashes. Not all crashes are super complex and require lasers to detect. Most are simple, routine, and can easily be prevented by today's vision based AI. In fact, evidence is mounting that computer vision based systems can actually outperform more traditional approaches to self-driving. Why? Because the low cost of cameras enables you to create a much larger, more varied, and more diverse dataset. If you want to have expensive custom cars that's fine, but you're going to get fewer vehicles for the same budget. Seeing what's in front of you now is actually less important than predicting what's going to happen next — and the large scale datasets used to train pure vision systems are the best for predicting what's next. Counter-intuitively, the simpler and lower cost sensor actually has properties that make it better suited for training advanced AI. Computer vision based self-driving is often framed by LIDAR proponents as "cheaping out" on the sensor suite to save money. But it's not about being cheap, it's about bringing the technology to everyone. 1.2 million people die on the road every year around the world. That's around 39 million people who've died on the roads around the world since I was born — equivalent to a city the size of Tokyo or New Delhi getting wiped off the map. The status quo is simply unacceptable, and something has to be done to fix it as soon as possible. Of the 1.2 million people that will die on the roads this year, about 40,000 will be Americans. That's about 3%. So if we moved entirely to self-driving cars in America and brought crashes down to 0, 97% of the world's crash fatalities would still be taking place as usual. Deploying a $200,000+ retrofitted self-driving car may work in a few American cities, but it is not going to make sense in most places around the world where fares are much cheaper. Most often, the choice is not between LIDAR and vision. It's between vision or nothing. The best system is the system that's there running on my car when I need it to save my life. To say that all self-driving cars must have LIDAR is to sentence most of the world to death. We can't write off computer vision if we want to make a serious dent in this problem. It's going to be a key piece of the solution. Let LIDAR based players build the best self-driving car they can, and let vision based players do the same. We need to be trying everything

Whole Mars Catalog

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Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast
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Quick demo of LFM 2.5 VL 1.6B model by Liquid AI that I recently added to Locally AI - Local AI Chat Running locally on iPhone 17 Pro at ~90tk/s with MLX Small vision-language models are improving fast

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