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相关概念视频

Language and Cognition01:27

Language and Cognition

676
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.
676
Observational Learning01:12

Observational Learning

779
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...
779
Language Development01:22

Language Development

799
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...
799
Cognitive Learning01:21

Cognitive Learning

957
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
957
Associative Learning01:27

Associative Learning

1.2K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.2K
Learning Disabilities01:25

Learning Disabilities

541
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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相关实验视频

Updated: Jan 7, 2026

Visualizing Visual Adaptation
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视觉语言模型中的双模式适应,用于持续学习.

Jiayang Zeng1, Wentao Zhang2, Kanghao Chen3

  • 1School of Computer Science and Engineering, Sun Yat-sen Univerisity, Guangzhou, China; Department of Network Intelligence, Pengcheng Laboratory, Shenzhen, China; Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China.

Neural networks : the official journal of the International Neural Network Society
|December 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了视觉语言模型 (VLM) 的新持续学习框架. 它通过调整图像和文本模式来增强知识获取并防止遗忘,优于现有的方法.

关键词:
课堂上的增量学习.持续的学习 持续的学习视觉语言模型的模型.

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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:

  • 持续学习使深度学习模型能够获得新的知识,而不会忘记以前的信息.
  • 视觉语言模型 (VLMs) 越来越多地被用于持续学习.
  • 当前的VLM持续学习方法往往会结一种模式,限制性能.

研究的目的:

  • 为VLMs提出一个新的持续学习框架,充分利用图像和文本模式.
  • 克服现有的VLM持续学习方法中单模适应的局限性.

主要方法:

  • 实施了双模式适应策略,使用特定任务的LoRA模块用于图像编码器和特定类的可学习文本提示用于文本编码器.
  • 通过LoRA模块在图像特征中增强了类内凝聚力.
  • 通过可学习的文本提示和即时重复使用的培训策略,改进了类间功能分离.

主要成果:

  • 拟议的方法在多个数据集上显著优于最先进的方法.
  • 在持续学习场景中证明有效的知识获取和灾难性遗忘缓解.
  • 展示了在VLM中双模式适应的好处.

结论:

  • 双模适应框架有效地利用了大规模预先训练有素的VLM在持续学习方面的潜力.
  • 拟议的方法为推进多式人工智能的持续学习提供了一个有希望的方向.
  • 未来的工作将包括发布代码供公众访问和进一步研究.