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相关概念视频

Language and Cognition01:27

Language and Cognition

343
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.
343
Components of Language01:24

Components of Language

269
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.
269
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.3K
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.2K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.2K
Language Development01:22

Language Development

359
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...
359
Lateralization01:28

Lateralization

330
Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
330

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相关实验视频

Updated: Jun 29, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

554

协作模式融合,以缓解视觉问题答案中的语言偏差.

Qiwen Lu1, Shengbo Chen1, Xiaoke Zhu1

  • 1School of Computer and Information Engineering, Henan University, Kaifeng 475001, China.

Journal of imaging
|March 27, 2024
PubMed
概括

本研究介绍了一种协作去偏差算法 (CoD),用于解决视觉问题答案 (VQA) 模型中的语言偏差. CoD 方法通过实现更好的多式联运知识融合,提高了模型的概括性和性能.

科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 自然语言处理自然语言处理.

背景情况:

  • 视觉问题答案 (VQA) 模型经常表现出语言偏差,依赖于虚假的相关性而不是真正的多式联络理解.
  • 这种偏差阻碍了模型的概括,并导致VQA任务的性能下降.
  • 现有的方法很难有效地减轻这种固有的语言偏见.

研究的目的:

  • 提出一种新的模式融合协作去偏差算法 (CoD),以解决VQA中的语言偏差.
  • 通过使他们能够充分利用多式联运信息来提高VQA模型的概括能力.
  • 通过减轻对表面相关性的依赖来提高VQA模型的预测准确性.

主要方法:

  • 开发了一个协作去偏差算法 (CoD),将偏差视为模式信息忽视.
  • 采用协作培训方法,以在不同模式之间进行相互建模.
  • 实施了高效的功能融合,以整合视觉和文本模式的信息.

主要成果:

  • 在多个VQA数据集 (VQA-CP v2,VQA v2,VQA-VS) 中证明了CoD算法的有效性.
  • 与基线模型相比,实现了更好的性能和概括性.
  • 使用CoD的基本基线模型在VQA-CP v2数据集上实现了60.14%的准确性.
关键词:
协作学习是一种协作式的学习.语言偏见 语言偏见视觉问题回答 回答问题

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结论:

  • 拟议的CoD算法有效地解决了VQA模型中的语言偏差.
  • 合作培训和模式融合对于利用多式联运知识和提高VQA绩效至关重要.
  • CoD 方法为开发更强大,更普遍的 VQA 系统提供了一个有希望的方向.