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Vision01:24

Vision

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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.
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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...
924
Encoding01:19

Encoding

882
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
882
Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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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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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.
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...
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関連する実験動画

Updated: Feb 15, 2026

Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding
09:14

Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding

Published on: August 22, 2016

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2Dエンコーダーのみを使用したデータ効率の高い3D医療視野言語モデル.

Yi Lian1, Yanchun Xie2, Yang Jiang3

  • 1Department of Orthopaedics, General Hospital of Northern Theater Command, Shenyang, China.

Scientific reports
|February 13, 2026
PubMed
まとめ
この要約は機械生成です。

この研究は,データ不足を克服するために2Dエンコーダーを使用して,3D医療ビジョン言語のタスクのためのデータ効率の良いフレームワークを導入します. この方法は,レポート作成と視覚的な質問応答で優れたパフォーマンスを達成します.

キーワード:
2Dビジョンエンコーダー 2Dビジョンエンコーダー3D医療イメージングデータ効率的な学習医学的な視覚言語モデル

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2D and 3D Echocardiography in the Axolotl Ambystoma Mexicanum
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Author Spotlight: Enhancing Skin Model Diversity with Cost-Effective 3D Cellular Models
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関連する実験動画

Last Updated: Feb 15, 2026

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2D and 3D Echocardiography in the Axolotl Ambystoma Mexicanum
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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • メディカルイマージング (医学イメージング)
  • コンピュータビジョン コンピュータビジョン

背景:

  • ビジョン・ランゲージ・モデル (VLMs) は,2D医療画像解析の成功を示しています.
  • VLMを3D医療データに拡張することは,限られた大規模な注釈データセットと,専門の3Dエンコーダに依存しているため,課題に直面しています.

研究 の 目的:

  • 3D医療視覚言語のタスクのためのデータ効率の良いフレームワークを開発する.
  • 3D専用のエンコーダの必要性を回避するために,事前訓練された2Dエンコーダを活用します.

主な方法:

  • 新しいパイプラインは,事前に訓練された2Dビジョンエンコーダーを使用して体積データを処理します.
  • 鍵となるステップは,スライス剪定のためのコサイヌ類似性,スライス間相関のための空間周波数融合,および細部を保存するために細粒子の特徴注入です.

主要な成果:

  • このフレームワークは,M3D-Capのレポート作成でMETEORスコア50.13を達成しました.
  • M3D-VQAで82.90%の精度を達成し,既存のモデルを上回りました.
  • 公的な3Dベンチマークで優れたパフォーマンスを実証した.

結論:

  • 提案されたフレームワークは,3D医療ビジョン言語のタスクのためのスケーラブルで効率的なパラダイムを提供します.
  • これは,データ集約的な3Dエンコーダーにデータ効率の良い代替手段を提供し,3D専用の予備訓練の必要性を回避します.