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Related Concept Videos

Language and Cognition01:27

Language and Cognition

551
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
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Translation01:31

Translation

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Natural Language Video Localization: A Revisit in Span-Based Question Answering Framework.

Hao Zhang, Aixin Sun, Wei Jing

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 23, 2021
    PubMed
    Summary

    This study introduces a novel span-based question answering approach for Natural Language Video Localization (NLVL). The proposed VSLNet and VSLNet-L models effectively locate moments in long videos, outperforming existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Existing Natural Language Video Localization (NLVL) methods, often vision-based, struggle with performance degradation on long videos.
    • Current approaches typically frame NLVL as ranking, anchor, or regression tasks.

    Purpose of the Study:

    • To propose a novel approach for NLVL by adapting a span-based question answering (QA) framework.
    • To develop a model, VSLNet, that effectively addresses the unique challenges of NLVL compared to traditional QA.

    Main Methods:

    • Treated untrimmed videos as text passages within a span-based QA framework.
    • Introduced a query-guided highlighting (QGH) strategy to focus search within relevant video segments.
    • Developed VSLNet-L with a multi-scale split-and-concatenation strategy to mitigate performance loss on long videos.

    Main Results:

    • VSLNet and VSLNet-L demonstrated superior performance over state-of-the-art methods on benchmark datasets.
    • VSLNet-L effectively addressed the performance degradation issue when localizing in long videos.
    • The span-based QA approach proved effective for the NLVL problem.

    Conclusions:

    • The span-based question answering framework offers a promising new direction for Natural Language Video Localization.
    • VSLNet and its extension VSLNet-L provide effective solutions for accurate video moment localization, especially in long videos.