统一的静态和动态网络:用于视频接地效率高的时间过
概括
本研究介绍了统一的静态和动态网络 (UniSDNet),用于高效的视频接地,使用人类视觉感知原则改善视频中的语义理解. 在自然语言和口语语言视频接地任务中,UniSDNet取得了最先进的结果.
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:
- 人类的视觉感知利用活动静音和持续活动机制来有效处理信息.
- 现有的视频接地方法经常在视频内容和跨模态查询之间进行全面的语义关联方面扎.
- 有效的视频接地对于视频检索和内容分析等应用程序至关重要.
研究的目的:
- 设计一个新的网络,UniSDNet,通过模仿人类视觉感知来有效地学习视频接地的语义关联.
- 改进静态和动态建模,以提高视频上下文理解和查询相关性.
- 在自然语言视频接地 (NLVG) 和口语视频接地 (SLVG) 中实现最先进的性能,同时提高推断速度.
主要方法:
- 开发了UniSDNet,结合了新的残余结构 (ResMLP) 来进行增强的静态建模和全球交互.
- 实施了灵感来自持续活动机制的动态建模,使用带有2D稀疏时间掩饰的视频剪辑图.
- 采用多核的时间高斯过器和元素级过卷积,用于复杂的上下文线索扩展和处理.
主要成果:
- 在多个NLVG和SLVG数据集上,UniSDNet实现了最先进的 (SOTA) 性能.
- 新创纪录包括ActivityNet标题上的38.88%R@1,IoU@0.7和TACoS上的40.26%R@1,IoU@0.5.
- 该模型显示,与强大的多查询基准相比,推断速度快1.56倍.
结论:
- UniSDNet提供了一种统一的方法,可以有效地进行视频接地,有效地整合静态和动态信息.
- 该网络的设计,灵感来自于人类的视觉感知,显著提高语义理解和上下文理解.
- 引入新的SLVG数据集和模型的效率有助于推进视频接地领域.
相关概念视频
01:21Linear Approximation in Time Domain
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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01:11Sampling Continuous Time Signal
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
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01:26Linear Approximation in Frequency Domain
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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01:23Linear time-invariant Systems
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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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01:22Basic Continuous Time Signals
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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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01:11Continuous -time Fourier Transform
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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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