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 ⬇️show more

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 ⬇️⬇️show more

YOLO training template repo reached >500 stars on Github,... that's crazy. Super happy to see people enjoy that project and start playing with computer vision and making their own models. Pothole detection is included as the demo workflow.show more

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 🧵show more

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 💗show more

In Florida, computer science and technology teacher Dan uses... Google AI to create personalized learning plans for his students, giving them the pathways they need to succeed.show more

😍 Love the famous AI VTuber Neuro-sama 💜 but... cant always talk to her? Made one for your self! 😏 First look on the Neuro-sama same AI voice for More details ⬇️show more

Our progress on computer vision tech to democratize and... annotate sports video data for accurate predictions, continues thanks to SkalskiP. Next step is sourcing and building an improved dataset to continue training the cv models for better accuracy and number (object) recognition.show more

We just shipped React Native ExecuTorch v0.8.0 – our... biggest release yet! 👏🏼 ➡️ Vision Camera integration that runs computer vision models directly on your camera feed, in realtime. Also new: multiple CV models (including RF-DETR (Roboflow) and Liquid AI's Vision Language), Bare React Native support, and more. Takes minutes to try – check out the repo! ⬇️show more

China is turning traditional massage into a robotics problem.... This robotic arm scans the body, maps pressure points, and then uses computer vision, force sensors and AI-controlled movements to perform techniques inspired by Traditional Chinese Medicine. It can press, knead, rub and push with controlled force, while sensing resistance or sudden movement and adjusting accordingly.show more

Introducing screengrasp - The new SOTA agent click position... model - beating Anthropic Computer Use, OmniParser and Molmo by a wide margin. - Simplified API access to the best click models on the market. 🔁Retweet and DM for free API credits. More details 👇show more

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-3.2-3Bshow more

Golden Age WarriorZ a time where WarriorZ travel Universe... to Universe looking for ones that possess super intelligence and powers far beyond the Norm to achieve their highest potentials for good or bad. Follow To Stay Updated For More Details…show more

Meta announces Muse Charm, a handheld gadget allowing people... to utilize an AI personal agent without their phone, computer or glasses. Available in time for the holidays.show more

Memo is a robot that uses AI to perform... household tasks effectively. Today Sunday announced its Series B, and we’re proud to be investors. Training robots for the home is hard — the environment is messy, dynamic, and full of edge cases. So Sunday is training robots directly on real households. Founders Tony Zhao and Cheng Chi built a glove-based system that lets hundreds of contributors record everyday tasks in their own homes, creating high-fidelity demonstrations that feed directly into robot learning. Home robotics will be defined by the companies that learn fastest from real homes. Sunday is building that loop. More here: Aaref Hilaly Amanda Huangshow more

🚀Thrilled to share what we’ve been building at TRI... over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!show more

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 everythingshow more