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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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相关实验视频

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Computer-Generated Animal Model Stimuli
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MIGC++:用于图像合成的高级多实例生成控制器.

Dewei Zhou, You Li, Fan Ma

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    我们介绍了多实例生成 (MIG),这是一个新任务,用于创建具有特定属性和位置的多个图像实例. 我们的方法,MIGC和MIGC++,克服了像属性泄漏这样的挑战,并确保了一致的代生成.

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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
    • 机器学习 机器学习

    背景情况:

    • 在图像中生成多个对象,并精确控制它们的属性和位置是一个具有挑战性的任务.
    • 现有的方法与属性泄露,多样化的实例描述以及在代生成过程中保持一致性作斗争.

    研究的目的:

    • 引入多实例生成 (MIG) 任务,用于生成多个精确控制的图像实例.
    • 开发解决MIG关键挑战的新方法,包括属性泄露,多样化的描述和代一致性.

    主要方法:

    • 为单实例任务提出多实例生成控制器 (MIGC),使用分割与征服策略.
    • 引入MIGC++以进行增强的属性控制 (文本/图像) 和位置控制 (框/面具).
    • 开发一致的MIG算法,以确保在代实例操作期间的一致性.

    主要成果:

    • 在COCO-MIG,多式联运MIG,COCO-Position和DrawBench基准上表现出与现有技术相比的显著性能改进.
    • 实现对实例位置,属性 (颜色,形状,类别) 和数量的精确控制.
    • 验证MIGC,MIGC++和Consistent-MIG在应对MIG挑战中的有效性.

    结论:

    • 拟议的MIG任务和相关方法 (MIGC,MIGC++,Consistent-MIG) 代表了控制图像生成的重大进展.
    • 这些方法提供了强大的解决方案,用于生成具有高保真性和用户指定的控制的多个实例.
    • 开发的基准标准有助于在多实例生成领域的未来研究和评估.