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相关概念视频

Vision01:24

Vision

53.3K
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.
53.3K
Language Development01:22

Language Development

366
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...
366
Observational Learning01:12

Observational Learning

173
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
173
Language and Cognition01:27

Language and Cognition

346
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.
346
Purposive Learning01:22

Purposive Learning

121
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
121
Introduction to Learning01:18

Introduction to Learning

394
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
394

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相关实验视频

Updated: Jul 4, 2025

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
06:07

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

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视觉语言模型的学习领域不变提示符

Cairong Zhao, Yubin Wang, Xinyang Jiang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 9, 2024
    PubMed
    概括

    MetaPrompt通过开发视觉语言模型的域不变提示来增强少量拍摄的学习. 这种方法提高了对新类和域的概括性,在跨数据集任务中优于现有方法.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 快速学习通过使用少数样本高效地适应大型视觉语言模型 (VLMs).
    • 当前的即时学习与将其推广到新的类和领域的斗争.
    • 现有的方法可以动态生成域特定提示,但错过跨域泛化潜力.

    研究的目的:

    • 介绍MetaPrompt,这是一个用于学习域不变提示的新型范式,用于在几次拍摄场景中进行提示.
    • 提高视觉语言模型提示的概括能力.
    • 解决当前关于跨领域和跨类概括的快速学习的局限性.

    主要方法:

    • 开发了一种双模态提示调网络,配合编码器,用于独立的图像和文本提示学习.
    • 采用了采用域内和域分割更新的替代插曲训练算法.
    • 引入了用于域内更新的非对称对比学习和用于跨域/跨类任务的域分割优化.

    主要成果:

    • MetaPrompt在基础到新概括的总和平均值上实现了1.02%的绝对收益.
    • 在4个数据集的域概括中,在基准上表现出一致的优势.
    • 在11个数据集中展示了有利的性能,用于基础到新通用化任务.

    更多相关视频

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    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
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    Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

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    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
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    Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

    Published on: December 14, 2012

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    结论:

    • MetaPrompt有效地学习域不变提示,显著改善了几次拍摄的概括.
    • 拟议的双模网络和交替的插曲培训提高了模型的适应性.
    • 在不同的场景中,MetaPrompt为强大的视觉语言模型适应提供了一个有希望的方向.