注意已成为深度学习方法中最常用的机制之一。注意机制可以帮助系统更多地关注功能空间的关键区域。例如,高振幅区域可以在语音情感识别(SER)中发挥重要作用。在本文中,我们确定了现有多头自我注意力中的注意力和信号幅度之间的不对对准。为了改善注意力区域,我们建议使用焦点注意(FA)机制和新型的校准注意(CA)机制,并结合多头自我注意力。通过FA机制,网络可以检测该细分市场中最大的振幅部分。通过采用CA机制,网络可以通过为每个注意力头分配不同权重来调节信息流并改善周围环境的利用。为了评估所提出的方法,使用IEMOCAP和RAVDESS数据集执行实验。实验结果表明,所提出的框架大大优于两个数据集上最新方法。
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学习表现力的表现对于深度学习至关重要。在言语情感识别(SER),真空区域或言语中的噪音干扰表达性的表示学习。但是,传统的基于RNN的模型容易受到这种噪音的影响。最近,图神经网络(GNN)证明了其在表示学习方面的有效性,我们为SER采用了此框架。特别是,我们提出了一个基于余弦的图形作为SER中表示学习的理想图形结构。我们提出了基于余弦相似性的图形卷积网络(COGCN),该网络对扰动和噪声是可靠的。实验结果表明,我们的方法优于最先进的方法或提供竞争性结果,仅使用1/30个参数降低模型大小。
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近年来,通过开发大型的深层模型,图像修复任务已经见证了绩效的巨大提高。尽管表现出色,但深层模型要求的重量计算限制了图像恢复的应用。为了提高限制,需要减少网络的大小,同时保持准确性。最近,N:M结构化修剪似乎是使模型具有准确性约束的有效且实用的修剪方法之一。但是,它无法解释图像恢复网络不同层的不同计算复杂性和性能要求。为了进一步优化效率和恢复精度之间的权衡,我们提出了一种新型的修剪方法,该方法确定了每一层N:M结构稀疏性的修剪比。关于超分辨率和脱张任务的广泛实验结果证明了我们方法的功效,该方法的表现胜过以前的修剪方法。拟议方法的Pytorch实施将在https://github.com/junghunoh/sls_cvpr2r2022上公开获得。
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过滤器修剪的目标是搜索不重要的过滤器以删除以便使卷积神经网络(CNNS)有效而不牺牲过程中的性能。挑战在于找到可以帮助确定每个过滤器关于神经网络的最终输出的重要或相关的信息的信息。在这项工作中,我们分享了我们的观察说,预先训练的CNN的批量标准化(BN)参数可用于估计激活输出的特征分布,而无需处理训练数据。在观察时,我们通过基于预先训练的CNN的BN参数评估每个滤波器的重要性来提出简单而有效的滤波修剪方法。 CiFar-10和Imagenet的实验结果表明,该方法可以在准确性下降和计算复杂性的计算复杂性和降低的折衷方面具有和不进行微调的卓越性能。
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量化图像超分辨率的深卷积神经网络大大降低了它们的计算成本。然而,现有的作品既不患有4个或低位宽度的超低精度的严重性能下降,或者需要沉重的微调过程以恢复性能。据我们所知,这种对低精度的漏洞依赖于特征映射值的两个统计观察。首先,特征贴图值的分布每个通道和每个输入图像都变化显着变化。其次,特征映射具有可以主导量化错误的异常值。基于这些观察,我们提出了一种新颖的分布感知量化方案(DAQ),其促进了超低精度的准确训练量化。 DAQ的简单功能确定了具有低计算负担的特征图和权重的动态范围。此外,我们的方法通过计算每个通道的相对灵敏度来实现混合精度量化,而无需涉及任何培训过程。尽管如此,量化感知培训也适用于辅助性能增益。我们的新方法优于最近的培训甚至基于培训的量化方法,以超低精度为最先进的图像超分辨率网络。
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The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.
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In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.
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Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.
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The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.
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According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.
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