Viso.ai

DECEMBER 6, 2024

Computer vision is a key component of self-driving cars. In this article, we’ll elaborate on how computer vision enhances these cars. To accomplish this, they require two key components: machine learning and computer vision. The eyes of the automobile are computer vision models.

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Viso.ai

DECEMBER 3, 2023

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. Object detection is a key field in artificial intelligence, allowing computer systems to “see” their environments by detecting objects in visual images or videos.

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Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). In this article, we dive into some of the most significant research papers that triggered the rapid development of computer vision.

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Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. Future trends and challenges Viso Suite is an end-to-end computer vision platform.

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Heartbeat

OCTOBER 9, 2023

Computer Vision for Cultural Heritage Preservation: Unlocking the Past with Advanced Imaging Technology Image Source: Technology Innovators Preserving our cultural legacy is critical because it allows us to remain in touch with our past, learn our roots, and appreciate humanity's rich history.

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Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

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Marktechpost

SEPTEMBER 14, 2024

Recent advances, however, have utilized deep learning to analyze large climate datasets and uncover complex patterns. Previous D&A studies have varied in methodology, with approaches including PCA analysis, regression, and machine learning models to identify climate fingerprints and assess warming trends.

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AWS Machine Learning Blog

APRIL 7, 2025

You can use state-of-the-art model architecturessuch as language models, computer vision models, and morewithout having to build them from scratch. yml file from the AWS Deep Learning Containers GitHub repository, illustrating how the model synthesizes information across an entire repository. billion to a projected $574.78

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PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role.

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Viso.ai

MAY 31, 2024

Pascal VOC is a renowned dataset and benchmark suite that has significantly contributed to the advancement of computer vision research. It provides standardized image data sets for object class recognition and a common set of tools for accessing the data and evaluating the performance of computer vision models.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

In recent years, the field of computer vision has witnessed significant advancements in the area of image segmentation. We can also get the bounding boxes from smaller models, or in some cases, using standard computer vision tools. His core interests include deep learning and serverless technologies.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Adversarial attacks pose a serious threat to the security of machine learning systems, as they can be used to manipulate the behavior of these systems in malicious ways.

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Viso.ai

JUNE 19, 2024

AlphaPose is a multi-person pose estimation model that uses computer vision and deep learning techniques to detect and predict human poses from images and videos in real time. About us: Viso Suite provides full-scale features to rapidly build, deploy, and scale enterprise-grade computer vision applications.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP).

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Mlearning.ai

JUNE 16, 2023

The ImageNet dataset, featuring natural images, contains 14,197,122 annotated images organized in 1000 classes and is commonly used as a benchmark for many computer vision models⁸. The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet.

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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. It is constructed by selecting 14 non-overlapping classes from DBpedia 2014.

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Viso.ai

JULY 17, 2024

Image analogies patch-based texture in-filling for artistic rendering – source The field of Neural style transfer took a completely new turn with Deep Learning. With deep learning, the results were impressively good. Here is the journey of NST. Gatys et al. 2015) The research paper by Leon A.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Our products provide capabilities to train deep neural network models and use them in a no-code environment. Learn more and request a demo.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture.

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Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. VGG-16: does the Visual Geometry Group develop an intense CNN architecture at the University of Oxford?

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Viso.ai

JULY 9, 2024

StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014. Before StyleGAN, NVIDIA did come up with the predecessor- ProGAN, however, this model could not fine-control the features of images generated.

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PyImageSearch

DECEMBER 4, 2023

Built in 2014 along the Ankhor Canal, it’s fondly known as the “Snow Mosque” due to its pristine white marble construction. To learn how to use Keras Core with different backends, read our blog post on Keras Core. The keras and keras_cv imports are for using Keras and its computer vision extensions, respectively.

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Mlearning.ai

JULY 31, 2023

Likewise, sound and text have no meaning to a computer. Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. 2014; Bojanowski et al., Sometimes, this can be easier and much faster. Mikolov, T.,

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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. For example, Dean Pomerleau used them to create a system that learned to drive a car [ 12 ]. (I The graph below shows the trend of publications in machine learning.

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ML Review

FEBRUARY 26, 2018

Tasks such as “I’d like to book a one-way flight from New York to Paris for tomorrow” can be solved by the intention commitment + slot filing matching or deep reinforcement learning (DRL) model. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Improve the quality and time to market for deep learning models in diagnostic medical imaging.

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Viso.ai

JUNE 26, 2024

This article will discuss the following: Neuromorphic Engineering and its core principles History and Development Algorithms Used How Neuromorphic Algorithms differ from Traditional Algorithms Real-world examples Applications and Use Cases About Us: At Viso.ai, we power Viso Suite, the most complete end-to-end computer vision platform.

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Sebastian Ruder

NOVEMBER 14, 2022

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. The Deep Learning Indaba 2022 in Tunesia. In Proceedings of the IEEE International Conference on Computer Vision (Vol. A framework for self-supervised learning of speech representations. In Proceedings of ICLR 2021.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

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Heartbeat

APRIL 18, 2023

It is a challenging task in computer vision, and it has many practical applications, such as video surveillance, human-computer interaction, sports analysis, and medical diagnosis. The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks.

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ML Review

MARCH 5, 2019

If the embedding vectors work as expected, computer vision papers should be closer together in this space, and reinforcement learning (RL) papers close to other RL papers. Note : The “Charts and Additional Insights” page, one chart being “Top topics from 2014 onwards”. 2014, January). Simple, like with like.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Founded in 2021, ThirdAI Corp.

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Viso.ai

DECEMBER 21, 2023

Over the past decade, the field of computer vision has experienced monumental artificial intelligence (AI) breakthroughs. This blog will introduce you to the computer vision visionaries behind these achievements. Viso Suite is the end-to-End, No-Code Computer Vision Solution.

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ODSC - Open Data Science

FEBRUARY 29, 2024

The Stanford AI Lab Founded in 1963, the Stanford AI Lab has made significant contributions to various domains, including natural language processing, computer vision, and robotics. Their research encompasses a broad spectrum of AI disciplines, including AI theory, reinforcement learning, and robotics. But that’s not all.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. Matei Zaharia is co-founder and Chief Technologist at Databricks as well as an Associate Professor of Computer Science at Stanford.

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