MOL:通过变压器 - 图形 - 风格卷积的微表达,光流和地标的联合估计
概括
这项研究引入了面部微表情识别 (MER) 的新型深度学习框架,其性能优于现有的方法. 这种方法有效地捕捉了微妙的面部肌肉动作,而不需要关键,提高了MER的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 情感计算是一种情感计算.
背景情况:
- 面部微表情识别 (MER) 是具有挑战性的,因为微妙的,短暂的行动.
- 现有的MER方法通常依赖于手工制作的功能或关键框架,并且受到小数据集的限制.
- 对MER的深度学习方法面临由于数据集规模和多样性的局限性.
研究的目的:
- 为MER提出一个端到端的微动作意识的深度学习框架.
- 开发一种新的特征提取块 (F5C),可以直接处理原始.
- 共同训练MER以光学流量估计和面部地标检测来增强微妙的动作捕捉.
主要方法:
- 开发了一种新的F5C块,将完全连接的卷积和通道对应卷积相结合,用于本地-全球特征提取.
- 利用变压器式和图形式卷积来提取局部特征和模型特征相关性.
- 实施了MER,光流估计和使用共享功能检测面部地标的联合培训.
主要成果:
- 拟议的框架显著优于CASME II,SAMM和SMIC基准的最先进的MER方法.
- 该模型在光学流量估计和面部地标检测方面表现强.
- 该框架成功地捕捉了与微表情相关的微妙的面部肌肉动作.
结论:
- 拟议的微动作意识深度学习框架为面部微表情识别提供了强大的解决方案.
- 配合辅助任务的联合培训有效地解决了MER中的数据稀缺问题.
- 新的F5C块可以从原始视频序列中直接,无关键的特征提取.
相关概念视频
01:24Convolution: Math, Graphics, and Discrete Signals
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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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01:25Transformers with Off-Nominal Turns Ratios
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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01:20Deconvolution
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
263
01:17Convolution Properties II
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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
292
01:20Convolution Properties I
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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
244
01:27Transformers in Distribution System
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
167