New Models Enable Prediction, Controllable World Generation and Reasoning for Physical AI Two New Blueprints Deliver Massive Physical AI Synthetic Data Generation for Robot and Autonomous Vehicle Post-Training 1X, Agility Robotics, Figure AI, Skild AI Among Early Adopters March 18, 2025—GTC—NVIDIA today announced a major release of new NVIDIA Cosmos™ world foundation models (WFMs), introducing […]
Expected to become the world’s largest such dataset, the initial release of standardized synthetic data is now available to robotics developers as open source. Teaching autonomous robots and vehicles how to interact with the physical world requires vast amounts of high-quality data. To give researchers and developers a head start, NVIDIA is releasing a massive,
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. With the recent advancements in generative AI and vision foundational models, VLMs present a new wave of visual computing wherein the models are capable of highly sophisticated perception and deep contextual understanding. These intelligent solutions offer a promising
This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. Video has become the lingua franca of the digital age, but its ubiquity presents a unique challenge: how do we efficiently extract meaningful information from this ocean of visual data? In Part 1 of this series, we navigate
Scalable Video Search: Cascading Foundation Models Read More +
This article was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. In today’s data-driven world, the ability to retrieve accurate information from even modest amounts of data is vital for developers seeking streamlined, effective solutions for quick deployments, prototyping, or experimentation. One of the key challenges in information retrieval
Building a Simple VLM-based Multimodal Information Retrieval System with NVIDIA NIM Read More +
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Vision language models (VLMs) are evolving at a breakneck speed. In 2020, the first VLMs revolutionized the generative AI landscape by bringing visual understanding to large language models (LLMs) through the use of a vision encoder. These
Vision Language Model Prompt Engineering Guide for Image and Video Understanding Read More +
This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In Part 2 of our Segment Anything Model 2 (SAM 2) Series, we show how foundation models (e.g., GPT-4o, Claude Sonnet 3.5 and YOLO-World) can be used to generate visual inputs (e.g., bounding boxes) for SAM 2. Learn
SAM 2 + GPT-4o: Cascading Foundation Models via Visual Prompting (Part 2) Read More +
This article was originally published at Tenyks’ website. It is reprinted here with the permission of Tenyks. In Part 1 of this article we introduce Segment Anything Model 2 (SAM 2). Then, we walk you through how you can set it up and run inference on your own video clips. Learn more about visual prompting
SAM 2 + GPT-4o: Cascading Foundation Models via Visual Prompting (Part 1) Read More +
This article was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. How the proliferation and evolution of generative models will transform the AI landscape and unlock value. The introduction of DeepSeek R1, a cutting-edge reasoning AI model, has caused ripples throughout the tech industry. That’s because its performance is on
AI Disruption is Driving Innovation in On-device Inference Read More +
This blog post was originally published at Tryolabs’ website. It is reprinted here with the permission of Tryolabs. From Face ID unlocking our phones to counting customers in stores, Computer Vision has already transformed how businesses operate. As Generative AI (GenAI) becomes more compelling and accessible, this tried-and-tested technology is entering a new era of
From Seeing to Understanding: LLMs Leveraging Computer Vision Read More +
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