AlexNet
AlexNet is a deep learning convolutional neural network, built by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton at the University of Toronto, that won the ImageNet Large Scale Visual Recognition…
Computer Vision
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Showing 1-53 of 53 articles
CLIP (Contrastive Language-Image Pre-training)
CLIP, short for Contrastive Language-Image Pre-training, is a family of neural networks developed by OpenAI researchers to learn a shared representation of images and natural-language descriptions.
Computer Vision
Computer vision is the study of computational methods that extract, estimate, or generate useful representations from visual measurements.
ControlNet
ControlNet is a neural network architecture that adds spatial and structural control to large pretrained text-to-image diffusion models.
ConvNeXt
ConvNeXt is a family of pure convolutional neural network (CNN) models that match or beat Vision Transformers on standard vision benchmarks
Convolutional Filter
A convolutional filter (also called a kernel or feature detector) is a small matrix of learnable weights that slides across an input and computes a dot product at each position to produce a feature map.
Convolutional Layer
A convolutional layer is the core building block of a convolutional neural network (CNN): it slides a small set of learnable filters (also called kernels) across the input, computing a convolution (technically…
Convolutional Neural Network
A convolutional neural network (CNN, or ConvNet) is a neural network that uses convolution-like linear operators in at least some layers.
DCGAN (Deep Convolutional GAN)
DCGAN (Deep Convolutional Generative Adversarial Network) is a family of generative adversarial network architectures, introduced in 2015 by Alec Radford, Luke Metz, and Soumith Chintala
DETR
DETR (DEtection TRansformer) is an end-to-end object detection model that reframes detection as a direct set prediction problem solved with a transformer encoder-decoder and bipartite matching, removing the…
DINO (computer vision)
DINO (self-DIstillation with NO labels) is a family of self-supervised learning methods for computer vision from Meta AI that trains Vision Transformers (ViTs) on unlabeled images and produces general-purpose…
DeepLab
DeepLab is a family of deep convolutional neural network architectures for semantic segmentation, developed by Liang-Chieh Chen and collaborators at UCLA and Google between 2014 and 2018 .
DeiT
DeiT (Data-efficient Image Transformers) is a family of vision transformer models that proved Vision Transformers can be trained to state-of-the-art image classification accuracy on ImageNet alone
DenseNet
DenseNet (Densely Connected Convolutional Networks) is a convolutional neural network architecture that connects every layer to every other layer in a feed-forward fashion
Depth estimation
Depth estimation is the computer vision task of predicting how far each surface in a scene is from the camera, producing a dense per-pixel depth map from one or more images.
Diffusion model
A diffusion model is a generative model that learns to transform samples from a simple reference distribution into samples resembling a data distribution by reversing a gradual corruption process.
EfficientNet
EfficientNet is a family of convolutional neural network architectures and a model-scaling method that uniformly scales network depth, width, and input resolution with a single compound coefficient, developed…
Faster R-CNN
Faster R-CNN is a two-stage object detection model introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun in the 2015 NeurIPS paper Faster R-CNN: Towards Real-Time Object Detection with Region…
Focal loss
Focal loss is a loss function that reshapes standard cross-entropy loss by adding a (1 - p_t)^gamma modulating factor, which down-weights well-classified (easy) examples so that training concentrates on hard
Gaussian Splatting
Gaussian splatting is a method for real-time radiance field rendering that represents a 3D scene as a collection of millions of anisotropic 3D Gaussian primitives, each defined by a position, covariance…
Grad-CAM
Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for producing visual explanations from convolutional neural network (CNN) models by using the gradients of a target class flowing into the…
Image Recognition
Image recognition is the field of artificial intelligence and computer vision that enables machines to identify, classify, and interpret the objects, patterns, and features contained in a digital image or…
Image classification
Image classification is the task of assigning a whole image to one category drawn from a fixed set of labels.
Image segmentation
Image segmentation is the computer vision task of partitioning a digital image into multiple regions by assigning every pixel a label, producing a pixel-level map of what each part of the image contains.
ImageNet
ImageNet is a large, hierarchically organized image dataset created to support research in computer vision, especially object recognition.
Inception (deep learning)
Inception is a family of convolutional neural network (CNN) architectures developed by researchers at Google, first introduced in 2014.
Instance segmentation
Instance segmentation is the computer vision task of detecting every object instance in an image and producing a pixel-precise mask for each one
Kaiming He
Kaiming He is a Chinese computer scientist known for foundational work in computer vision and deep learning, and is most closely associated with deep residual networks, or ResNet.
Latent diffusion model
A latent diffusion model (LDM) is a type of diffusion model that runs the denoising diffusion process in a compressed latent space learned by a pretrained autoencoder, rather than directly in pixel space…
LeNet
LeNet is the pioneering family of convolutional neural networks developed by Yann LeCun and collaborators at AT&T Bell Labs between roughly 1988 and 1998 to read handwritten characters
Mask R-CNN
Mask R-CNN is a deep convolutional neural network for instance segmentation, introduced in 2017 by Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick at Facebook AI Research (FAIR).
MobileNet
MobileNet is a family of efficient convolutional neural network (CNN) architectures developed by Google for mobile and edge AI applications.
Multimodal Model
A multimodal model is a machine learning model, or a model-based system, that processes, relates, or produces information across more than one kind of data. Each kind is called a modality.
NeRF
Neural Radiance Fields (NeRF) is a method for synthesizing photorealistic novel views of a 3D scene by encoding the scene as a continuous 5D function (3D position plus 2D viewing direction) inside a single…
OCR Models
OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through Optical Character Recognition (OCR).
Object detection
Object detection is a computer vision task that finds instances of interest in an image and assigns each one a category.
Panoptic Segmentation
Panoptic segmentation is a computer vision task that assigns every pixel in an image a semantic class label and, for countable objects, an instance identity.
Pose estimation
Pose estimation is the computer vision task of detecting and localizing the keypoints (also called landmarks or joints) of a human body, hand, face, animal, or rigid object in images and video, then connecting…
Pre-training
Pre-training is a stage of machine learning in which a model learns parameters from a source dataset or source objective before those parameters are reused or adapted for a target use.
R-CNN (Regions with CNN features)
R-CNN (short for Regions with CNN features) is a two-stage object detection method that generates about 2,000 candidate region proposals per image with Selective Search, warps each region and runs a…
ResNet
ResNet, short for residual network, is a family of deep convolutional neural networks introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016.
SSD (Single Shot MultiBox Detector)
SSD (Single Shot MultiBox Detector) is a single stage object detection model that predicts bounding boxes and per class confidence scores in one forward pass through a convolutional network
Saliency map
A saliency map is an explainable AI visualization that highlights which parts of an input, most often the individual pixels of an image, most influenced a deep learning model's prediction.
Semantic Segmentation
Semantic segmentation is a computer vision task that assigns a category label to every single pixel in an image, producing a dense map in which each pixel carries the identity of the object class it belongs to.
SimCLR
SimCLR (Simple Framework for Contrastive Learning of Visual Representations) is a self-supervised learning method for computer vision in which a network is trained to recognise that two differently augmented…
Stride
Stride is the step size by which a filter (or pooling window) moves across the input in a convolutional neural network (CNN): a stride of 1 shifts the filter one position at a time and visits every location…
Swin Transformer
The Swin Transformer (Shifted Window Transformer) is a hierarchical vision transformer architecture that computes self-attention within local, non-overlapping windows and introduces a shifted window…
Translational invariance
Translational invariance (also called translation invariance or shift invariance) is the property of a function, system, or machine learning model whose output does not change when its input is translated…
U-Net
U-Net is a convolutional neural network architecture designed for biomedical image segmentation.
VGG
VGG (also called VGGNet) is a deep convolutional neural network architecture, introduced in 2014 by Karen Simonyan and Andrew Zisserman of the Visual Geometry Group at the University of Oxford
Vision Encoder
A vision encoder is the neural network component that turns an image into a sequence of numerical vectors that other models can consume.
YOLO (object detection)
YOLO (You Only Look Once) is a family of object detection models that treat detection as a single regression problem, predicting bounding boxes and class probabilities directly from full images in one forward…
YOLOv8
YOLOv8 is a family of one-stage computer vision models released by Ultralytics on January 10, 2023.