深度神经网络一直是分类任务成功的推动力，例如对象和音频识别。许多最近提出的架构似乎已经取得了令人印象深刻的结果和概括，其中大多数似乎是断开连接的。在这项工作中，我们在统一框架下对深层分类器进行了研究。特别是，我们以输入的不同程度多项式的形式表达最新的结构（例如残留和非本地网络）。我们的框架提供了有关每个模型的电感偏差的见解，并可以在其多项式性质上进行自然扩展。根据标准图像和音频分类基准评估所提出模型的功效。提出的模型的表达性既是在增加模型性能和模型压缩方面都突出的。最后，在存在有限的数据和长尾数据分布的情况下，此分类法所允许的扩展显示。我们希望这种分类法可以在现有特定领域的架构之间提供联系。源代码可在\ url {https://github.com/grigorisg9gr/polynomials-for-aigmenting-nns}中获得。

音频合成中的时频（TF）表示已越来越多地通过实价网络建模。但是，忽略TF表示的复杂值的性质可能会导致次优性能，并且需要其他模块（例如，用于对阶段进行建模）。为此，我们介绍了称为Apollo的复杂价值的多项式网络，该网络以自然方式集成了这种复杂值的表示。具体而言，阿波罗使用高阶张量作为缩放参数捕获输入元件的高阶相关性。通过利用标准张量分解，我们得出了不同的体系结构并启用建模更丰富的相关性。我们概述了这样的体系结构，并在四个基准测试中展示了它们在音频发电中的性能。重点，阿波罗（Apollo）在音频生成中SC09数据集中的最先进的扩散模型比对抗方法的$ 17.5 \％$改进，而$ 8.2 \％$。我们的模型可以鼓励在复杂领域的其他高效体系结构进行系统的设计。

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a queryindependent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. The code and configurations are released at https://github.com/xvjiarui/GCNet.

现有的多尺度解决方案会导致仅增加接受场大小的风险，同时忽略小型接受场。因此，有效构建自适应神经网络以识别各种空间尺度对象是一个具有挑战性的问题。为了解决这个问题，我们首先引入一个新的注意力维度，即除了现有的注意力维度（例如渠道，空间和分支）之外，并提出了一个新颖的选择性深度注意网络，以对称地处理各种视觉中的多尺度对象任务。具体而言，在给定神经网络的每个阶段内的块，即重新连接，输出层次功能映射共享相同的分辨率但具有不同的接收场大小。基于此结构属性，我们设计了一个舞台建筑模块，即SDA，其中包括树干分支和类似SE的注意力分支。躯干分支的块输出融合在一起，以通过注意力分支指导其深度注意力分配。根据提出的注意机制，我们可以动态选择不同的深度特征，这有助于自适应调整可变大小输入对象的接收场大小。这样，跨块信息相互作用会导致沿深度方向的远距离依赖关系。与其他多尺度方法相比，我们的SDA方法结合了从以前的块到舞台输出的多个接受场，从而提供了更广泛，更丰富的有效接收场。此外，我们的方法可以用作其他多尺度网络以及注意力网络的可插入模块，并创造为SDA- $ x $ net。它们的组合进一步扩展了有效的接受场的范围，可以实现可解释的神经网络。我们的源代码可在\ url {https://github.com/qingbeiguo/sda-xnet.git}中获得。

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our nonlocal models can compete or outperform current competition winners on both Kinetics and Charades datasets.In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available.

神经切线内核（NTK）是分析神经网络及其泛化界限的训练动力学的强大工具。关于NTK的研究已致力于典型的神经网络体系结构，但对于Hadamard产品（NNS-HP）的神经网络不完整，例如StyleGAN和多项式神经网络。在这项工作中，我们为特殊类别的NNS-HP（即多项式神经网络）得出了有限宽度的NTK公式。我们证明了它们与关联的NTK与内核回归预测变量的等效性，该预测扩大了NTK的应用范围。根据我们的结果，我们阐明了针对外推和光谱偏置，PNN在标准神经网络上的分离。我们的两个关键见解是，与标准神经网络相比，PNN能够在外推方案中拟合更复杂的功能，并承认相应NTK的特征值衰减较慢。此外，我们的理论结果可以扩展到其他类型的NNS-HP，从而扩大了我们工作的范围。我们的经验结果验证了更广泛的NNS-HP类别的分离，这为对神经体系结构有了更深入的理解提供了良好的理由。

视觉变压器的最新进展在基于点产生自我注意的新空间建模机制驱动的各种任务中取得了巨大成功。在本文中，我们表明，视觉变压器背后的关键要素，即输入自适应，远程和高阶空间相互作用，也可以通过基于卷积的框架有效地实现。我们介绍了递归封闭式卷积（$ \ textit {g}^\ textit {n} $ conv），该卷积{n} $ conv）与封闭的卷积和递归设计执行高阶空间交互。新操作是高度灵活和可定制的，它与卷积的各种变体兼容，并将自我注意的两阶相互作用扩展到任意订单，而无需引入大量额外的计算。 $ \ textit {g}^\ textit {n} $ conv可以用作插件模块，以改善各种视觉变压器和基于卷积的模型。根据该操作，我们构建了一个名为Hornet的新型通用视觉骨干家族。关于ImageNet分类，可可对象检测和ADE20K语义分割的广泛实验表明，大黄蜂的表现优于Swin变形金刚，并具有相似的整体体系结构和训练配置的明显边距。大黄蜂还显示出对更多训练数据和更大模型大小的有利可伸缩性。除了在视觉编码器中的有效性外，我们还可以将$ \ textit {g}^\ textit {n} $ conv应用于特定于任务的解码器，并始终通过较少的计算来提高密集的预测性能。我们的结果表明，$ \ textIt {g}^\ textit {n} $ conv可以成为视觉建模的新基本模块，可有效结合视觉变形金刚和CNN的优点。代码可从https://github.com/raoyongming/hornet获得

由于存储器和计算资源有限，部署在移动设备上的卷积神经网络（CNNS）是困难的。我们的目标是通过利用特征图中的冗余来设计包括CPU和GPU的异构设备的高效神经网络，这很少在神经结构设计中进行了研究。对于类似CPU的设备，我们提出了一种新颖的CPU高效的Ghost（C-Ghost）模块，以生成从廉价操作的更多特征映射。基于一组内在的特征映射，我们使用廉价的成本应用一系列线性变换，以生成许多幽灵特征图，可以完全揭示内在特征的信息。所提出的C-Ghost模块可以作为即插即用组件，以升级现有的卷积神经网络。 C-Ghost瓶颈旨在堆叠C-Ghost模块，然后可以轻松建立轻量级的C-Ghostnet。我们进一步考虑GPU设备的有效网络。在建筑阶段的情况下，不涉及太多的GPU效率（例如，深度明智的卷积），我们建议利用阶段明智的特征冗余来制定GPU高效的幽灵（G-GHOST）阶段结构。舞台中的特征被分成两个部分，其中使用具有较少输出通道的原始块处理第一部分，用于生成内在特征，另一个通过利用阶段明智的冗余来生成廉价的操作。在基准测试上进行的实验证明了所提出的C-Ghost模块和G-Ghost阶段的有效性。 C-Ghostnet和G-Ghostnet分别可以分别实现CPU和GPU的准确性和延迟的最佳权衡。代码可在https://github.com/huawei-noah/cv-backbones获得。

事实证明，超复杂的神经网络可以减少参数的总数，同时通过利用Clifford代数的特性来确保有价值的性能。最近，通过涉及有效的参数化kronecker产品，超复合线性层得到了进一步改善。在本文中，我们定义了超复杂卷积层的参数化，并介绍了轻巧有效的大型大型模型的参数化超复杂神经网络（PHNN）。我们的方法直接从数据中掌握了卷积规则和过滤器组织，而无需遵循严格的预定义域结构。 Phnns可以灵活地在任何用户定义或调谐域中操作，无论代数规则是否是预设的，从1D到$ n $ d。这样的锻造性允许在其自然域中处理多维输入，而无需吞并进一步的尺寸，而是在Quaternion神经网络中使用3D输入（例如颜色图像）。结果，拟议中的Phnn家族以$ 1/n $的参数运行，因为其在真实域中的类似物。我们通过在各种图像数据集上执行实验以及音频数据集证明了这种方法对应用程序多个域的多功能性，在这些实验中，我们的方法的表现优于真实和Quaternion值值。完整代码可在以下网址获得：https：//github.com/elegan23/hypernets。

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we aim to develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to selfattention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameterlimited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutional models.

建模长期依赖关系对于理解计算机视觉中的任务至关重要。尽管卷积神经网络（CNN）在许多视觉任务中都表现出色，但由于它们通常由当地核层组成，因此它们仍然限制捕获长期结构化关系。但是，完全连接的图（例如变形金刚中的自我发项操作）对这种建模是有益的，但是，其计算开销非常有用。在本文中，我们提出了一个动态图形消息传递网络，与建模完全连接的图形相比，该网络大大降低了计算复杂性。这是通过在图表中自适应采样节点（以输入为条件）来实现的，以传递消息传递。基于采样节点，我们动态预测节点依赖性滤波器权重和亲和力矩阵，以在它们之间传播信息。这种公式使我们能够设计一个自我发挥的模块，更重要的是，我们将基于变压器的新骨干网络用于图像分类预处理，并用于解决各种下游任务（对象检测，实例和语义细分）。使用此模型，我们在四个不同任务上的强，最先进的基线方面显示出显着改进。我们的方法还优于完全连接的图形，同时使用较少的浮点操作和参数。代码和型号将在https://github.com/fudan-zvg/dgmn2上公开提供。

在过去的十年中，CNN在电脑愿景世界中统治了至高无上，但最近，变压器一直在崛起。然而，自我关注的二次计算成本已成为实践应用中的严重问题。在没有CNN的情况下，在这种情况下已经有很多研究了，并且在这种情况下自我关注。特别地，MLP混合器是使用MLP设计的简单架构，并击中与视觉变压器相当的精度。然而，这种体系结构中唯一的归纳偏见是嵌入令牌。这叶打开了将非卷积（或非本地）电感偏差结合到架构中的可能性，因此我们使用了两个简单的想法，以便利用其捕获全局相关能力的同时将电感偏差纳入MLP混合器。一种方法是将令牌混合块垂直和水平分割。另一种方法是在一些令牌混合通道中进行空间相关性密集。通过这种方法，我们能够提高MLP混合器的准确性，同时降低其参数和计算复杂性。 RAFTMLP-S的小型模型与每个计算的参数和效率方面的基于最先进的全球MLP的模型相当。此外，我们通过利用双向插值来解决基于MLP的模型的固定输入图像分辨率的问题。我们证明这些模型可以应用于诸如物体检测的下游任务的架构的骨干。但是，它没有显着的性能，并提到了对基于全球MLP的模型的下游任务的特定MLP特定架构的需求。 pytorch版本中的源代码可用于\ url {https:/github.com/okojoalg/raft-mlp}。

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance, which inevitably increase model complexity.To overcome the paradox of performance and complexity trade-off, this paper proposes an Efficient Channel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a local crosschannel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore, we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. The proposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs. 24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensively evaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. The experimental results show our module is more efficient while performing favorably against its counterparts.

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight Ghost-Net can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https: //github.com/huawei-noah/ghostnet.

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt [67] evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in "compute" 1 time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.

最近，开发了EAGL-I系统是为了迅速创建大量标记的植物数据集，该数据集旨在被农民和研究人员普遍使用，以创建农业中的AI驱动解决方案。结果，由40,000张图像组成的公开植物识别数据集与系统一起创建了由8种植物物种组成的不同尺寸的图像，以证明其能力。本文提出了一种新颖的方法，称为可变重叠的时间连续滑动窗口（fotcsw），该方法将由图像组成的图像转换为具有可变大小的图像的数据集，为3D表示，具有适合卷积神经网络的固定大小，并证明了此表示形式是比将数据集的图像调整到给定尺寸的信息更丰富。我们从理论上正式化了该方法的用例及其固有的属性，我们证明了它对数据具有过采样和正则化效果。通过将Fotcsw方法与最近提出的称为1维多项式神经网络的机器学习模型的3D扩展相结合，我们能够创建一个模型，该模型在数据集中创建的数据集中达到了99.9％的最新精度， EAGL-I系统超过了众所周知的建筑，例如重新系统和启动。此外，我们创建了一种启发式算法，该算法能够降低任何预先训练的N维多项式神经网络，并在不改变其性能的情况下压缩它，从而使模型更快，更轻。此外，我们确定当前可用的数据集无法以目前的形式用于机器学习，这是因为训练集和测试集之间存在很大的类不平衡。因此，我们创建了一个特定的预处理和模型开发框架，使我们能够将准确性从49.23％提高到99.9％。

量子多体系统的状态是在高维的希尔伯特空间中定义的，可以对子系统之间的丰富而复杂的相互作用进行建模。在机器学习中，复杂的多个多线性相关性也可能存在于输入功能中。在本文中，我们提出了一个量子启发的多线性模型，称为残留张量列（RESTT），以捕获单个模型中从低阶到高阶的特征的多次多线性相关性。 RESTT能够在高维空间中建立强大的决策边界，以解决拟合和分类任务。特别是，我们证明了完全连接的层和Volterra系列可以将其视为特殊情况。此外，我们得出了根据平均场分析来稳定RESTT训练的权重初始化规则。我们证明，这样的规则比TT的规则放松得多，这意味着休息可以轻松解决现有TT模型中存在的消失和爆炸梯度问题。数值实验表明，RESTT的表现优于最先进的张量网络，并在MNIST和时尚MNIST数据集上进行基准深度学习模型。此外，RESTT在两个实践示例上的统计方法比其他有限数据的统计方法更好，这些方法具有复杂的功能相互作用。

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the "Squeezeand-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-ofthe-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251%, achieving a ∼25% relative improvement over the winning entry of 2016.

Semiconductor manufacturing is on the cusp of a revolution: the Internet of Things (IoT). With IoT we can connect all the equipment and feed information back to the factory so that quality issues can be detected. In this situation, more and more edge devices are used in wafer inspection equipment. This edge device must have the ability to quickly detect defects. Therefore, how to develop a high-efficiency architecture for automatic defect classification to be suitable for edge devices is the primary task. In this paper, we present a novel architecture that can perform defect classification in a more efficient way. The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism. We named this method as self-proliferation-and-attention neural network. This method has been successfully applied to various defect pattern classification tasks. Compared with other latest methods, SP&A-Net has higher accuracy and lower computation cost in many defect inspection tasks.

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3.is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design.Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on ImageNet [1] classification, COCO object detection [2], VOC image segmentation [3]. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as actual latency, and the number of parameters.