Computer Vision (CV)

These terms represent key concepts, tasks, and technologies within the field of computer vision, highlighting the focus on enabling machines to interpret and understand the visual world.

Action Recognition - The process of identifying and categorizing actions in videos or sequences of images, a fundamental task in computer vision.

Autoencoder - Although used in various contexts, in computer vision, autoencoders are used for tasks such as dimensionality reduction of images, denoising, and feature learning.

Convolutional Neural Network (CNN) - A specialized kind of neural network that is particularly effective for processing data with a grid-like topology, such as images, making it a cornerstone of modern computer vision.

DALL-E - An AI model developed by OpenAI that generates images from textual descriptions, showcasing the intersection of natural language processing and computer vision.

Facial Recognition - The use of computer vision technology to identify or verify individuals from digital images or video frames.

Feature Engineering - This process involves identifying and extracting useful attributes from visual data to improve model performance, crucial in enhancing the effectiveness of predictive models.

Feature Learning - In computer vision, this involves algorithms learning to automatically identify and use the relevant features in images for tasks like classification or recognition.

Image Recognition - The ability of AI to identify objects, places, people, writing, and actions in images.

Rotation Prediction - A self-supervised learning task used in computer vision where a model is trained to predict the rotation applied to an input image, aiding in learning general features about the visual world.

Supervised Learning - This methodology uses labeled datasets to train models on tasks like image classification, essential for teaching models to correctly interpret visual data.

Video Data - Refers to the use and analysis of video data within artificial intelligence and machine learning, particularly within the domain of computer vision for tasks such as action recognition, motion analysis, and video summarization.

Video Summarization - The process of creating a short summary that captures the essential elements of a video, a task that involves understanding and interpreting visual content, making it part of computer vision.