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関連する概念動画

Language and Cognition01:27

Language and Cognition

438
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.
438
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

1.0K
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
1.0K
Language Development01:22

Language Development

444
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.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
444
Visual System01:26

Visual System

684
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
684
Vision01:24

Vision

55.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
55.3K
Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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一般化可能な視覚言語モデルのための構造誘導のグラデント調整

Juncheng Li, Minghe Gao, Siliang Tang

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    |September 1, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    Gradient-Regulated Meta-prompt learning (GRAM) は,視覚言語モデルの迅速なチューニングを強化し,メタラーニングとグラデーションの調節により,数ショットでの適応とクロスドメインの汎用性を改善します.

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    科学分野:

    • 人工知能
    • コンピュータ・ビジョン
    • 自然言語処理

    背景:

    • プロンプト・チューニングは 訓練済みの視覚言語モデルを ソフト・プロンプトで効率的に調整します
    • 初期化感度とオーバーフィットで課題に直面しています.
    • 既存の方法は,データ不足のシナリオで迅速な適応と一般化を維持するために苦労しています.

    研究 の 目的:

    • グラデント制御メタプロンプト (GRAM) フレームワークを導入する.
    • ショット数とショットゼロの学習シナリオでプロンプトチューニングの有効性を高めます.
    • 多領域の汎用性を向上させ,視覚言語モデルのオーバーフィッティングを減らす.

    主な方法:

    • 弱いラベルの画像・テキストデータを用いたメタラーニングパラダイムを開発した.
    • データの組織化のためにクロスモダル・ヒエラルキカル・クラスタリングを使用.
    • 汎用性を向上させるためのグラデント制御機能が導入されました.
    • 試験時間調整のための構造誘発グラデント調節機能を提案した.

    主要な成果:

    • GRAMは最先端のショットとゼロショットの一般化を実現します.
    • このフレームワークは,さまざまな迅速なチューニング方法を一貫して改善します.
    • 有効な適応が証明されているが,データは限られている.
    • 様々な領域にわたる 強力なメタラーニングを展示しました

    結論:

    • GRAMは,迅速なチューニングの課題に 堅牢で適応可能なソリューションを提供しています.
    • 提案された方法は,少数のショットとゼロショットの学習能力を大幅に向上させます.
    • GRAMは,プロンプトチューニングのパフォーマンスを高めるためのモデルアグノスティックなアプローチを提供します.
    • このフレームワークは,明示的な注釈なしに効率的な知識の移転を容易にする.