Instance segmentation is a challenging computer vision task that requires the prediction of object instances and their per-pixel segmentation mask. This makes it a hybrid of semantic segmentation and object detection.Ever since Mask R-CNN was invented, the state-of-the-art method for instance segmentation has largely been Mask RCNN and its variants … [Read more...] about Single Stage Instance Segmentation – A Review
Computer Vision
The Highest-Trending Research Papers From CVPR 2020
CVPR 2020 is yet another big AI conference that takes place 100% virtually this year. But regardless of the format, the conference still showcases the most interesting cutting-edge research ideas in computer vision and image generation.Here we’ve picked up the research papers that started trending within the AI research community months before their actual presentation at … [Read more...] about The Highest-Trending Research Papers From CVPR 2020
Deep Transfer Learning for Image Classification
The following tutorial covers how to set up a state of the art deep learning model for image classification. The approach is based on the machine learning frameworks “Tensorflow” and “Keras”, and includes all the code needed to replicate the results in this tutorial.The prerequisites for setting up the model is access to labelled data, and as an example case I have used … [Read more...] about Deep Transfer Learning for Image Classification
Convolutional Neural Networks With Heterogeneous Metadata
In autonomous driving, convolutional neural networks are the go-to tool for various perception tasks. Although CNNs are great at distilling information from camera images (or a sequence of them in form of a video clip), I constantly bump into all kinds of metadata that do not lend themselves to convolutional neural networks.Metadata, by traditional definition, means a set … [Read more...] about Convolutional Neural Networks With Heterogeneous Metadata
Demystifying Object Detection And Instance Segmentation For Data Scientists
I like deep learning a lot but Object Detection is something that doesn’t come easily to me.And Object detection is important and does have its uses. Most common of them being self-driving cars, medical imaging and face detection.It is definitely a hard problem to solve. And with so many moving parts and new concepts introduced over the long history of this problem, it … [Read more...] about Demystifying Object Detection And Instance Segmentation For Data Scientists
Generating New Faces With Variational Autoencoders
IntroductionDeep generative models are gaining tremendous popularity, both in the industry as well as academic research. The idea of a computer program generating new human faces or new animals can be quite exciting. Deep generative models take a slightly different approach compared to supervised learning which we shall discuss very soon.This tutorial covers the basics … [Read more...] about Generating New Faces With Variational Autoencoders
Novel AI Methods For Video Generation for Marketing & Advertising
This research summary is part of our AI for Marketing series which covers the latest AI & machine learning approaches to 5 aspects of marketing automation:AttributionOptimizationPersonalizationAnalyticsContent Generation: ImagesContent Generation: VideosContent Generation: TextAI algorithms can significantly increase the efficiency of the original content generation … [Read more...] about Novel AI Methods For Video Generation for Marketing & Advertising
New Ways To Leverage AI Image Synthesis For Marketing & Advertising Content
This research summary is part of our AI for Marketing series which covers the latest AI & machine learning approaches to 5 aspects of marketing automation:AttributionOptimizationPersonalizationAnalyticsContent Generation: ImagesContent Generation: VideosContent Generation: TextAfter Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow in 2014, a … [Read more...] about New Ways To Leverage AI Image Synthesis For Marketing & Advertising Content
4 CNN Networks Every Machine Learning Engineer Should Know
Over the years, variants of CNN architectures have been developed, leading to amazing advances in the field of deep learning. A good measure of this progress is the error rates in competitions such as the ILSVRC ImageNet challenge. In this competition, the top-5 error rate for image classification fell from over 26% to less than 3%. In this article, we will look at some of the … [Read more...] about 4 CNN Networks Every Machine Learning Engineer Should Know
CNNs, Part 2: Training a Convolutional Neural Network
In this post, we’re going to do a deep-dive on something most introductions to Convolutional Neural Networks (CNNs) lack: how to train a CNN, including deriving gradients, implementing backprop from scratch (using only numpy), and ultimately building a full training pipeline!This post assumes a basic knowledge of CNNs. My introduction to … [Read more...] about CNNs, Part 2: Training a Convolutional Neural Network