Agentic video understanding
Agentic video understanding is an approach to video analysis in which a reasoning model chooses which parts of a video to inspect for a particular question.
Machine Learning
Explore learning methods, model architectures, datasets, and practical applications.
Explore articles
Browse subtopics: Computer Vision
Articles that also belong to these categories. Counts cover all of Machine Learning.
All articles (858)Deep Learning (187)Natural Language Processing (92)Statistics (90)Training & Optimization (70)Data & Datasets (65)Neural Networks (58)Reinforcement Learning (58)Model Evaluation (55)Artificial Intelligence (54)Large Language Models (49)Mathematics (48)Computer Vision (41)AI Ethics (35)AI Infrastructure (29)AI Safety (26)Open Source AI (25)Data Science (24)AI Benchmarks (22)Developer Tools (20)Algorithms (18)AI Hardware (17)AI Tools & Products (17)Google (16)Generative AI (14)Information Retrieval (14)Model Architecture (13)People (13)AI Agents (12)Interpretability (12)Robotics (12)AI Companies (11)Transformer Models (11)AI Inference (10)MLOps (10)AI Models (8)Computer Science (8)AI Code Generation (7)AI Research (7)Multimodal AI (7)Speech & Audio AI (7)Chinese AI (6)AI Policy & Regulation (5)Meta AI (5)Programming Languages (5)AI History (4)AI for Science (4)Prompt Engineering (4)AI Alignment (3)AI Events (3)Reasoning Models (3)Embodied AI (2)Software Development (2)2025 in artificial intelligence (1)Anthropic (1)Diffusion Models (1)Drug Discovery (1)Education AI (1)Enterprise AI (1)Healthcare AI (1)Image Generation (1)NVIDIA (1)Small Language Models (1)World Models (1)
Showing 1-41 of 41 articles
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…
Average Precision
Average precision (AP) is an evaluation metric that summarizes the precision-recall curve of a ranked list into a single number between 0 and 1, equal to the area under that curve.
Bounding Box
A bounding box is a rectangular region defined by a set of coordinates that encloses an object of interest within an image, video frame, or three-dimensional space.
CIDEr
CIDEr (Consensus-based Image Description Evaluation) is an automatic evaluation metric for image captioning that scores a machine-generated caption by how closely it matches the consensus of several human…
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.
COCO dataset
COCO (Common Objects in Context) is a large-scale dataset for object detection, image segmentation, keypoint detection, and image captioning.
Computer Vision
Computer vision is the study of computational methods that extract, estimate, or generate useful representations from visual measurements.
Computer-use agent
A computer-use agent (CUA) is a category of AI agent in artificial intelligence that performs tasks by directly operating a general-purpose computer's graphical user interface (GUI) the way a human does, by…
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.
Convolutional Operation
The convolutional operation is a mathematical procedure that combines two functions to produce a third function expressing how the shape of one is modified by the other.
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…
Depthwise Separable CNN
A depthwise separable convolution is a factorized form of convolution that decomposes a standard convolutional operation into two sequential steps: a depthwise convolution and a pointwise convolution.
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.
Earth Mover's Distance
Earth Mover's Distance (EMD), also known as the Wasserstein-1 distance, Kantorovich-Rubinstein metric, or Mallows's distance
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
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…
I-JEPA
I-JEPA (Image-based Joint-Embedding Predictive Architecture) is a self-supervised learning method for computer vision developed by Meta AI.
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.
LAION
LAION (Large-scale Artificial Intelligence Open Network) is a German non-profit organization
Landmarks
Landmarks are reference points used as anchors in two largely separate areas of machine learning.
LeVJEPA
LeVJEPA is a self-supervised learning method for training video encoders. It applies the LeJEPA objective to video
MNIST
The Modified National Institute of Standards and Technology (MNIST) database is a collection of 70,000 grayscale images of handwritten digits (0 through 9) that has served as one of the most widely used…
Machine learning terms/Computer Vision
The key machine learning terms for computer vision describe how neural networks turn pixels into predictions: a convolution slides a small learned filter (kernel) across an image to detect patterns, a…
Masked autoencoder (MAE)
Masked autoencoder (MAE) is a self-supervised learning method for vision transformers that masks roughly 75% of an input image's patches and trains a network to reconstruct the missing pixels from the small…
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.
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.
PASCAL VOC
PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) is a long-running benchmark dataset and annual challenge for object recognition, object detection…
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.
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…
Spatial Pooling
Spatial pooling is a downsampling operation in convolutional neural networks (CNNs) that replaces a local region of a feature map with a single summary statistic, such as the maximum or the average of the…
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…
U-Net
U-Net is a convolutional neural network architecture designed for biomedical image segmentation.
V-JEPA
V-JEPA (Video Joint Embedding Predictive Architecture) is a self-supervised video model from Meta AI that learns by predicting masked regions of a video in an abstract latent representation space rather than…