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A Decade of Transformation: How Deep Learning Redefined Stereo Matching in the Twenties

A fundamental topic in computer vision for nearly half a century, stereo matching involves calculating dense disparity maps from two corrected pictures. It plays a critical role in many applications, including autonomous driving, robotics, and augmented reality, among many others. According to their cost-volume computation and optimization methodologies, existing surveys categorize end-to-end architectures into 2D…

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Hyperion: A Novel, Modular, Distributed, High-Performance Optimization Framework Targeting both Discrete and Continuous-Time SLAM Applications

In robotics, understanding the position and movement of a sensor suite within its environment is crucial. Traditional methods, called Simultaneous Localization and Mapping (SLAM), often face challenges with unsynchronized sensor data and require complex computations. These methods must estimate the position at discrete time intervals, making it difficult to handle data from various sensors that…

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Reinforcement Learning, Part 5: Temporal-Difference Learning | by Vyacheslav Efimov | Jul, 2024

Intelligently synergizing dynamic programming and Monte Carlo algorithms R einforcement learning is a domain in machine learning that introduces the concept of an agent learning optimal strategies in complex environments. The agent learns from its actions, which result in rewards, based on the environment’s state. Reinforcement learning is a challenging topic and differs significantly from…

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How to automate medical data extraction: A quick guide

Medical data extraction remains a significant hurdle, with the sector requiring 7.7x more administrative workers than other industries. Automating healthcare data extraction can help healthcare providers reduce operational spending and streamline their processes while improving patient care. These systems capture and extract crucial information from a variety of medical documents—patient records, insurance forms,…

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From OCR to Deep Learning

In today's data-driven world, forms are everywhere, and form data extraction has become crucial. These documents collect information efficiently but often require manual processing. That's where intelligent document processing (IDP) comes in. IDP leverages OCR, AI, and ML to automate form processing, making data extraction faster and more accurate than traditional methods. It's not always…

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Mastering Prompt Engineering: Leveraging the Power of Generative AI | by Niño Ross Rodriguez | Jul, 2024

Generative artificial intelligence (GenAI) is revolutionising the tech industry. Different platforms are now capable of generating output based on a few lines of text. Midjourney can create stunning images, Synthesia can produce dynamic visuals and scenes, and the popular ChatGPT can assist with coding. As GenAI grows and gains popularity at an exponential rate, should…

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Data Orchestration: The Dividing Line Between Generative AI Success and Failure

Sponsored Content       As organizations strive to leverage Generative AI, they often encounter a gap between its promising potential and realizing actual business value. At Astronomer, we’ve seen firsthand how integrating generative AI (GenAI) into operational processes can transform businesses. But we’ve also observed that the key to success lies in orchestrating…

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Enhancing Vision-Language Models: Addressing Multi-Object Hallucination and Cultural Inclusivity for Improved Visual Assistance in Diverse Contexts

The research on vision-language models (VLMs) has gained significant momentum, driven by their potential to revolutionize various applications, including visual assistance for visually impaired individuals. However, current evaluations of these models often need to pay more attention to the complexities introduced by multi-object scenarios and diverse cultural contexts. Two notable studies shed light on these…

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