VerbA verb () is a word (part of speech) that in syntax generally conveys an action (bring, read, walk, run, learn), an occurrence (happen, become), or a state of being (be, exist, stand). In the usual description of English, the basic form, with or without the particle to, is the infinitive. In many languages, verbs are inflected (modified in form) to encode tense, aspect, mood, and voice. A verb may also agree with the person, gender or number of some of its arguments, such as its subject, or object.
ImageAn image is a visual representation of something. An image can be a two-dimensional (2D) representation, such as a drawing, painting, or photograph, or a three-dimensional (3D) object, such as a carving or sculpture. An image may be displayed through other media, including projection on a surface, activation of electronic signals, or digital displays. Two-dimensional images can be still or animated. Still images can usually be reproduced through mechanical means, such as photography, printmaking or photocopying.
English phrasal verbsIn the traditional grammar of Modern English, a phrasal verb typically constitutes a single semantic unit composed of a verb followed by a particle (examples: turn down, run into or sit up), sometimes combined with a preposition (examples: get together with, run out of or feed off of). Alternative terms include verb-adverb combination, verb-particle construction, two-part word/verb or three-part word/verb (depending on the number of particles) and multi-word verb.
Pattern recognitionPattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Correspondence problemThe correspondence problem refers to the problem of ascertaining which parts of one image correspond to which parts of another image, where differences are due to movement of the camera, the elapse of time, and/or movement of objects in the photos.
Compound verbIn linguistics, a compound verb or complex predicate is a multi-word compound that functions as a single verb. One component of the compound is a light verb or vector, which carries any inflections, indicating tense, mood, or aspect, but provides only fine shades of meaning. The other, "primary", component is a verb or noun which carries most of the semantics of the compound, and determines its arguments. It is usually in either base or [in Verb + Verb compounds] conjunctive participial form.
Handwriting recognitionHandwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning (optical character recognition) or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.
Text-to-image modelA text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Such models began to be developed in the mid-2010s, as a result of advances in deep neural networks. In 2022, the output of state of the art text-to-image models, such as OpenAI's DALL-E 2, Google Brain's , StabilityAI's Stable Diffusion, and Midjourney began to approach the quality of real photographs and human-drawn art.
Stative verbAccording to some linguistics theories, a stative verb is a verb that describes a state of being, in contrast to a dynamic verb, which describes an action. The difference can be categorized by saying that stative verbs describe situations that are static or unchanging throughout their entire duration, whereas dynamic verbs describe processes that entail change over time. Many languages distinguish between these two types in terms of how they can be used grammatically.
Generative modelIn statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, following : A generative model is a statistical model of the joint probability distribution on given observable variable X and target variable Y; A discriminative model is a model of the conditional probability of the target Y, given an observation x; and Classifiers computed without using a probability model are also referred to loosely as "discriminative".
Optical character recognitionOptical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of s of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Discriminative modelDiscriminative models, also referred to as conditional models, are a class of logistical models used for classification or regression. They distinguish decision boundaries through observed data, such as pass/fail, win/lose, alive/dead or healthy/sick. Typical discriminative models include logistic regression (LR), conditional random fields (CRFs) (specified over an undirected graph), decision trees, and many others. Typical generative model approaches include naive Bayes classifiers, Gaussian mixture models, variational autoencoders, generative adversarial networks and others.
Auxiliary verbAn auxiliary verb (abbreviated ) is a verb that adds functional or grammatical meaning to the clause in which it occurs, so as to express tense, aspect, modality, voice, emphasis, etc. Auxiliary verbs usually accompany an infinitive verb or a participle, which respectively provide the main semantic content of the clause. An example is the verb have in the sentence I have finished my lunch. Here, the auxiliary have helps to express the perfect aspect along with the participle, finished.
Language modelA language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.
SubtitlesSubtitles are text representing the contents of the audio in a film, television show, opera or other audiovisual media. Subtitles might provide a transcription or translation of spoken dialogue. Although naming conventions can vary, captions are subtitles that include written descriptions of other elements of the audio like music or sound effects. Captions are thus especially helpful to people who are deaf or hard-of-hearing. Other times, subtitles add information not present in the audio.
Intransitive verbIn grammar, an intransitive verb is a verb whose context does not entail a direct object. That lack of transitivity distinguishes intransitive verbs from transitive verbs, which entail one or more objects. Additionally, intransitive verbs are typically considered within a class apart from modal verbs and defective verbs. In the following sentences, verbs are used without a direct object: "Rivers flow." "I sneezed." "My dog ran." "Water evaporates when it's hot." "You've grown since I last saw you!" "I wonder how old I will be when I die.
Impersonal verbIn linguistics, an impersonal verb is one that has no determinate subject. For example, in the sentence "It rains", rain is an impersonal verb and the pronoun it does not refer to anything. In many languages the verb takes a third person singular inflection and often appears with an expletive subject. In the active voice, impersonal verbs can be used to express operation of nature, mental distress, and acts with no reference to the doer. Impersonal verbs are also called weather verbs because they frequently appear in the context of weather description.
Regular and irregular verbsA regular verb is any verb whose conjugation follows the typical pattern, or one of the typical patterns, of the language to which it belongs. A verb whose conjugation follows a different pattern is called an irregular verb. This is one instance of the distinction between regular and irregular inflection, which can also apply to other word classes, such as nouns and adjectives. In English, for example, verbs such as play, enter, and like are regular since they form their inflected parts by adding the typical endings -s, -ing and -ed to give forms such as plays, entering, and liked.
