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相关概念视频

The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty in Measurement: Significant Figures03:34

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Unc-SOD:用于小物体检测的不确定性学习框架.

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    此摘要是机器生成的。

    本研究介绍了Unc-SOD,这是一个用于小物体检测 (SOD) 的新型框架. 它有效地模拟不确定性,以改善小物体的识别,在关键基准上取得最先进的结果.

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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:

    • 小物体检测 (SOD) 具有挑战性,因为信息区域有限,并且在小实例中存在固有的模糊性.
    • 现有的两阶段探测器在SOD中扎着不确定性和特征不一致.
    • 当前的采样方法可能会被不可识别的小目标误导,浪费计算资源.

    研究的目的:

    • 为小型物体检测 (Unc-SOD) 开发一个不确定性学习框架.
    • 解决小物体检测中不确定性和特征不一致性的挑战.
    • 为了提高在复杂场景中检测小物体的准确性和效率.

    主要方法:

    • 将辅助不确定性分支纳入传统的区域提案网络 (RPN),以建模实例级不确定性.
    • 利用不确定性作为提案网络的动态抽样标准,改善候选人选择.
    • 设计了一种感知与交互策略,通过优化区域特征来捕捉丰富和歧视性的表征.

    主要成果:

    • 在SODA-D和SODA-A基准上,UNC-SOD取得了最先进的表现.
    • 该框架显示了COCO,VisDrone和清华-讯100K数据集的显著改进.
    • 结果表明,在处理小实例时,与当前检测器相比,其性能优越.

    结论:

    • Unc-SOD有效地模拟和利用不确定性来改进小物体检测.
    • 拟议的框架为具有挑战性的SOD任务提供了一个强大的解决方案.
    • 这项工作代表了将不确定性建模纳入SOD的开创性尝试,产生了显著的性能增长.