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NVIDIA AI Releases HOVER: A Breakthrough AI for Versatile Humanoid Control in Robotics

The future of robotics has advanced significantly. For many years, there have been expectations of human-like robots that can navigate our environments, perform complex tasks, and work alongside humans. Examples include robots conducting precise surgical procedures, building intricate structures, assisting in disaster response, and cooperating efficiently with humans in various settings such as factories, offices,…

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From a Point to L∞

Why you should read this  As someone who did a Bachelors in Mathematics I was first introduced to L¹ and L² as a measure of Distance… now it seems to be a measure of error — where have we gone wrong? But jokes aside, there seems to be this misconception that L₁ and L₂ serve the same function — and…

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ViSMaP: Unsupervised Summarization of Hour-Long Videos Using Meta-Prompting and Short-Form Datasets

Video captioning models are typically trained on datasets consisting of short videos, usually under three minutes in length, paired with corresponding captions. While this enables them to describe basic actions like walking or talking, these models struggle with the complexity of long-form videos, such as vlogs, sports events, and movies that can last over an…

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University of Michigan Researchers Introduce OceanSim: A High-Performance GPU-Accelerated Underwater Simulator for Advanced Marine Robotics

Marine robotic platforms support various applications, including marine exploration, underwater infrastructure inspection, and ocean environment monitoring. While reliable perception systems enable robots to sense their surroundings, detect objects, and navigate complex underwater terrains independently, developing these systems presents unique difficulties compared to their terrestrial counterparts. Collecting real-world underwater data requires complex hardware, controlled experimental setups,…

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NVIDIA AI Releases Describe Anything 3B: A Multimodal LLM for Fine-Grained Image and Video Captioning

Challenges in Localized Captioning for Vision-Language Models Describing specific regions within images or videos remains a persistent challenge in vision-language modeling. While general-purpose vision-language models (VLMs) perform well at generating global captions, they often fall short in producing detailed, region-specific descriptions. These limitations are amplified in video data, where models must account for temporal…

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