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相关概念视频

Parallel Processing01:20

Parallel Processing

179
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
179
Visual System01:26

Visual System

613
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...
613
Perception01:28

Perception

499
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
499
Vision01:24

Vision

53.5K
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.
53.5K

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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全球和本地交互感知网络用于引用图像分割.

Jing Liu, Hongchen Tan, Yongli Hu

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    此摘要是机器生成的。

    本研究介绍了一种用于引用图像分割 (RIS) 的新网络,该网络可以改进语言和图像信息的结合方式. 全球和本地交互感知网络 (GLIPN) 通过考虑详细和整体图像背景来增强理解.

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    科学领域:

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

    背景情况:

    • 引用图像细分 (RIS) 需要语言和图像模式的有效融合.
    • 当前的方法可能难以整合本地和全球语义信息.

    研究的目的:

    • 提出一个新的RIS网络,全球和本地交互感知网络 (GLIPN).
    • 从当地和全球的角度来看,提高语言和形象之间的模式融合的质量.

    主要方法:

    • 引入了全球和本地互动感知 (GLIP) 计划.
    • 开发了一个局部感知模块 (LPM) 用于文字图像局部语义对应.
    • 开发了一个全球感知模块 (GPM),将全球图像结构集成到融合中.

    主要成果:

    • 拟议的GLIPN显著增强了当地和全球的模式融合.
    • 对基准数据集的实验表明GLIPN的表现优于现有的最先进的方法.
    • GLIP计划有效地结合了当地和全球背景语义.

    结论:

    • GLIPN为引用图像分割提供了一种有效的方法.
    • GLIP计划为RIS中的多模式融合提供了一个强大的框架.
    • 该方法通过利用本地和全球上下文信息来证明卓越的性能.