Viso.ai

FEBRUARY 21, 2024

Graph Neural Networks (GNNs) are a type of neural network designed to directly operate on graphs, a data structure consisting of nodes (vertices) and edges connecting them. In this article, we’ll start with a gentle introduction to Graph Neural Networks and follow with a comprehensive technical deep dive.

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Viso.ai

OCTOBER 15, 2024

Get a personal demo. However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs). It first extracts feature maps from a picture through a Convolutional Neural Network (CNN). Get in touch and request a demo for your organization.

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Viso.ai

FEBRUARY 25, 2024

To learn more, book a demo. Following that, the development of Convolutional Neural Networks (CNNs) was a watershed moment in the field. The introduction of the Super-Resolution Convolutional Neural Network (SRCNN) later demonstrated that deep learning models could outperform traditional image resolution methods.

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The MLOps Blog

NOVEMBER 14, 2023

Example of a deep learning visualization: small convolutional neural network CNN, notice how the thickness of the colorful lines indicates the weight of the neural pathways | Source How is deep learning visualization different from traditional ML visualization? Let’s take a computer vision model as an example.

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Viso.ai

DECEMBER 1, 2024

Get a demo. Camera-based mask detection Tumor Detection Brain tumors can be seen in MRI scans and are often detected using deep neural networks. Automatic image-based plant disease severity estimation using Deep convolutional neural network (CNN) applications was developed, for example, to identify apple black rot.

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Viso.ai

OCTOBER 10, 2024

Get a demo for your organization. In supervised learning, images are annotated to train neural networks – Image Annotation with Viso Suite What Is the Goal of Pattern Recognition? The third major approach is based on the technology of artificial neural networks ( ANN ), named Neural Pattern Recognition.

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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). Book a demo to learn more about how Viso Suite can help solve business problems. Faster R-CNN as a single, unified network for object detection.

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Viso.ai

OCTOBER 7, 2024

Computer vision mainly uses neural networks under the hood. Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e., It powers autonomous drones, self-driving vehicles, face recognition in CCTV cameras, etc.

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Viso.ai

FEBRUARY 11, 2024

2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks. Object Detection and Instance Segmentation – DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network, published by Xuan Shen et al.,

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Viso.ai

DECEMBER 1, 2023

Get the Whitepaper or a Demo. A deep learning model, or a DL model, is a neural network that has been trained to learn how to perform a task, such as recognizing objects in digital images and videos, or understanding human speech. To learn more about this AI model, read our guide about how Deep Neural Network models work.

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Viso.ai

OCTOBER 10, 2024

To get started with Viso Suite, book a demo with our team of experts. We will elaborate on computer vision techniques like Convolutional Neural Networks (CNNs). They applied clustering in combination with deep neural networks to provide pseudo-labels for a convolutional neural network.

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Viso.ai

AUGUST 5, 2024

It uses a Region Proposal Network (RPN) and Convolutional Neural Networks (CNNs) to identify and locate objects in complex real-world images. Get a demo. Convolution Neural Network (CNN) A Convolutional Neural Network is a type of deep neural network that detects objects in the image.

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Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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Viso.ai

DECEMBER 20, 2023

Get a demo for your organization. Common algorithms and techniques in supervised learning include Neural Networks , Support Vector Machine (SVM), Logistic Regression, Random Forest, or Decision Tree algorithms. About us: Viso.ai Object detection with tracking in a real-time application built on the Viso Suite Platform.

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Viso.ai

FEBRUARY 23, 2024

Get a demo. Interactive Segmentation – Source Popular Image Segmentation Models Mask-RCNN The Mask Region-based Convolutional Neural Network (RCNN) was one of the most popular segmentation algorithms during Computer Vision’s early days. Book a demo to learn more about the Viso Suite.

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Mlearning.ai

MARCH 10, 2023

A Haar-Feature is just like a kernel in convolutional neural-network (CNN), except that in a CNN, the values of the kernel are determined by training, while a Haar-Feature is manually determined. On the other hand, neural networks are a type of machine learning algorithm inspired by the structure of the human brain.

Neural Network 52

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Viso.ai

DECEMBER 3, 2023

Get a demo for your organization. Today, the most powerful image processing models are based on convolutional neural networks (CNNs). A popular library that uses neural networks for real-time human pose estimation in 3D, even for multi-person use cases, is named OpenPose. What Is Pose Estimation?

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Viso.ai

NOVEMBER 27, 2024

Object detection is a computer vision task that uses neural networks to localize and classify objects in images. Multiple machine-learning algorithms are used for object detection, one of which is convolutional neural networks (CNNs). To learn more, book a demo with our team.

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Viso.ai

FEBRUARY 8, 2024

Get a demo here. Viso Suite is the end-to-end, no-code computer vision platform Idea Behind Deep Belief Networks Deep belief networks consist of various layers of neurons, each connected to the neuron of the subsequent layer. Book a demo to learn more about the Viso suite. Faster training and better results.

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Viso.ai

OCTOBER 17, 2024

Both John Hopfield and Geoffrey Hinton conducted foundational research on artificial neural networks (ANNs). To start implementing computer vision for business solutions, book a demo of Viso Suite with our team of experts. Mathematics, computer science, biology, and physics form the roots of machine learning and neural networks.

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Get a demo for your company. Use Cases and applications of Vision Transformers About us: Viso.ai

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Viso.ai

AUGUST 1, 2024

To learn more about how Viso Suite can help automate your business needs, book a demo with our team. Feature Extraction with a Convolutional Neural Network (CNN): In this first step of the process, DensePose passes the given image into a pre-trained Convolutional Neural Network (CNN), such as ResNet.

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Viso.ai

DECEMBER 19, 2024

To learn more about how you can leverage enterprise-grade computer vision for business applications, we invite you to book a demo with our team of experts. These tasks typically utilize deep learning models, an artificial neural network. A simple three-layered neural network.

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Viso.ai

SEPTEMBER 1, 2024

To get started, book a demo with our team of experts. Get Started With Enterprise-Grade Computer Vision To start using Viso Suite for your AI initiatives, book a demo with our team. With a platform that covers all stages of the application development lifecycle. We’ll discuss your use case and how Viso Suite can help solve it.

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Viso.ai

FEBRUARY 25, 2023

Get a personal demo. Especially with the emergence of Deep Neural Networks (DNN) , image segmentation applications have made tremendous progress. Read more about the basics of a Convolutional Neural Network. Instance Segmentation Most popular image segmentation datasets About us: Viso.ai

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Viso.ai

JANUARY 16, 2024

Get a demo here. Matching Networks: The algorithm computes embeddings using a support set, and one-shot learns by classifying the query data sample based on which support set embedding is closest to the query embedding – source. The embedding functions can be convolutional neural networks (CNNs).

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Viso.ai

FEBRUARY 27, 2024

Farhadi, signifying a step forward in the real-time object detection space, outperforming its predecessor – the Region-based Convolutional Neural Network (R-CNN). It is a single-pass algorithm having only one neural network to predict bounding boxes and class probabilities using a full image as input.

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Viso.ai

JUNE 19, 2024

Get your demo here! However, when deep learning became popular in the 2010s, DeepPose was introduced by researchers at Facebook in 2014, this was an inspirational model that utilized Convolutional Neural Networks ( CNNs ) to effectively detect human poses directly from images. This leads to high accuracy.

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Viso.ai

JULY 9, 2024

Book a demo to learn more. As the name suggests, this technique involves transferring the learnings of one trained machine learning model to another, in the form of neural network weights. To understand how transfer learning works, it is essential to understand the architecture of Deep Neural Networks.

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Viso.ai

DECEMBER 22, 2023

The Segment Anything Model Technical Backbone: Convolutional, Generative Networks, and More Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) play a foundational role in the capabilities of SAM. This is essential for its high accuracy and efficiency in image segmentation.

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Viso.ai

APRIL 4, 2024

Dynamic Neural Networks use optimization methods to arrive at the target. Deep Learning with Convolutional Neural Network – Source For example, image classification models use the image’s RGB values to produce classes with a confidence score. To learn more, book a demo with our team.

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Viso.ai

MARCH 20, 2024

Learn more about Viso Suite by booking a demo with us. Foundation models are large-scale neural network architectures that undergo pre-training on vast amounts of unlabeled data through self-supervised learning. Thus, eliminating the need for time-consuming, complex point solutions.

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Viso.ai

JULY 9, 2024

Learn more and book a demo with us. Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. About us: Viso Suite is the only end-to-end computer vision infrastructure.

Neural Network 52

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Viso.ai

JULY 17, 2024

An image can be represented by the relationships between the activations of features detected by a convolutional neural network (CNN). Previous methods directly fed the semantic layout as input to the deep neural network, which then the model processed through stacks of convolution, normalization, and nonlinearity layers.

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Viso.ai

JUNE 10, 2024

Book a demo with our team of experts to learn more. Deep learning models use neural networks to analyze motion from video data. Feature Extraction with Deep Learning Convolutional Neural Networks ( CNNs ) can be used to extract features such as edges, corners, and textures from images or video frames.

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Viso.ai

MAY 6, 2024

To learn more, get a personalized demo from the Viso team. In particular, researchers are working with the following deep learning models to enhance spatio-temporal action recognition systems: Convolutional Neural Networks (CNNs) In a basic sense, spatial recognition systems use CNNs to extract features from pixel data.

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Viso.ai

JULY 9, 2024

Get a personal demo. Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests. Autoencoders are a type of neural network that simply copies the input to the output.

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