Our team reviewed the papers accepted to NeurIPS 2020 and shortlisted the most interesting ones across different research areas. Here are the topics we cover:Natural Language Processing & Conversational AIComputer VisionReinforcement Learning & MoreTackling COVID-19 with AI & Machine LearningIf you’re interested in the remarkable keynote presentations, … [Read more...] about NeurIPS 2020: Key Research Papers in Computer Vision
Computer Vision
Novel Computer Vision Research Papers From 2020
Will transformers revolutionize computer vision like they did with natural language processing? That’s one of the major research questions investigated by computer vision scientists in 2020. The first results indicate that transformers achieve very promising results on image recognition tasks.Beyond transformers in vision applications, we also noticed a continuous … [Read more...] about Novel Computer Vision Research Papers From 2020
Graph Convolutional Networks (GCN)
In this post, we’re gonna take a close look at one of the well-known graph neural networks named Graph Convolutional Network (GCN). First, we’ll get the intuition to see how it works, then we’ll go deeper into the maths behind it.Why Graphs?Many problems are graphs in true nature. In our world, we see many data are graphs, such as molecules, social … [Read more...] about Graph Convolutional Networks (GCN)
ECCV 2020: Some Highlights
The 2020 European Conference on Computer Vision took place online, from 23 to 28 August, and consisted of 1360 papers, divided into 104 orals, 160 spotlights and the rest of 1096 papers as posters. In addition to 45 workshops and 16 tutorials. As it is the case in recent years with ML and CV conferences, the huge number of papers can be overwhelming at times. Similar to … [Read more...] about ECCV 2020: Some Highlights
Best Research Papers From ICML 2020
This year’s virtual ICML conference hosted 10800+ attendees from 75 countries. Apparently, the virtual format makes big research conferences such as ICML more accessible to the AI community all over the world.With almost 5000 research papers submitted to ICML 2020 and an acceptance rate of 21.8%, a total of 1088 papers were presented at the conference. As usual, the … [Read more...] about Best Research Papers From ICML 2020
Single Stage Instance Segmentation — A Review
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
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