Computer vision (CV) is a field where machines learn to “see” and understand images or videos. It helps machines recognize objects, faces, and even actions in photos or videos. For example, CV is used in self-driving cars to detect road signs and people, or in medical scans to spot diseases. Training a CV model from […]
Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. […]
One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given […]
Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to […]
Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers […]
Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is […]
Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades […]
Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. The Matterport Mask R-CNN project provides a library that […]
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are […]
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are […]
