Viso.ai

OCTOBER 15, 2024

In the field of real-time object identification, YOLOv11 architecture is an advancement over its predecessor, the Region-based Convolutional Neural Network (R-CNN). Using an entire image as input, this single-pass approach with a single neural network predicts bounding boxes and class probabilities.

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Google Research AI blog

JANUARY 18, 2023

I will begin with a discussion of language, computer vision, multi-modal models, and generative machine learning models. Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. Let’s get started!

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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). Fast Style Transfer (2016) While the previous model produced decent results, it was computationally expensive and slow. Johnson et al. What is Perceptual Loss?

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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. Divvala, R.

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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). Viso Suite is the end-to-End, No-Code Computer Vision Solution.

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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). Attention can enable our inspection and debugging of networks.

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Viso.ai

DECEMBER 18, 2023

YOLOv8 is the newest model in the YOLO algorithm series – the most well-known family of object detection and classification models in the Computer Vision (CV) field. offers the world’s leading end-to-end no-code Computer Vision Platform Viso Suite. Get a demo.

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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. Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. December 10, 2016.

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Viso.ai

MAY 20, 2023

The YOLOv7 algorithm is making big waves in the computer vision and machine learning communities. It requires several times cheaper hardware than other neural networks and can be trained much faster on small datasets without any pre-trained weights. The original YOLO object detector was first released in 2016.

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Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

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Viso.ai

MARCH 11, 2024

Object detection is a fundamental task in computer vision, and YOLOX plays a fair role in improving it. YOLO in 2015 became the first significant model capable of object detection with a single pass of the network. The previous approaches relied on Region-based Convolutional Neural Network (RCNN) and sliding window techniques.

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Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Stanford University and panel researchers P. Stone and R. Brooks et al. Brooks et al. Moreover, they can answer any question and communicate naturally.

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Lexalytics

APRIL 5, 2021

While working as an RA in the computer vision group, I had the opportunity to sit in a robotic Humvee as it used Pomerleau’s code to drive around the University of Massachusetts’ stadium.) The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) Hinton (again!)

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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. Its proposed neural architecture can provide fairly accurate answers to natural language questions about images.

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Sebastian Ruder

JANUARY 19, 2021

More recently, contrastive learning gained popularity in self-supervised representation learning in computer vision and speech ( van den Oord, 2018 ; Hénaff et al., 2016 ; Webster et al., 8) Image Transformers The Vision Transformer ( Dosovitskiy et al., 2020 ; Wallace et al., 2020 ; Carlini et al.,

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. spaCy’s default token-vector encoding settings are a depth 4 convolutional neural network with width 96, and hash embeddings with 2000 rows. assert doc[3:6].text

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AI Weekly

JUNE 29, 2023

In the News Next DeepMind's Algorithm To Eclipse ChatGPT IN 2016, an AI program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. June 15, 2023 /PRNewswire/ -- Quantum Computing Inc. ("QCi" Powered by pluto.fi

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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). 2016) — “ LipNet: End-to-End Sentence-level Lipreading.” [17]

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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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Bugra Akyildiz

NOVEMBER 17, 2024

This uses advanced computer vision techniques, specifically a Vision Transformer model, to analyze and organize photos of properties. Vision Transformers(ViT) ViT is a type of machine learning model that applies the transformer architecture, originally developed for natural language processing, to image recognition tasks.

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