The convolution layer has been the dominant feature extractor in computer vision for years. However, the spatial aggregation in convolution is basically a pattern matching process that applies fixed filters which are inefficient at modeling visual elements with varying spatial distributions. This paper presents a new image feature extractor, called the local relation layer, that adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. With this relational approach, it can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference. A network built with local relation layers, called the Local Relation Network (LR-Net), is found to provide greater modeling capacity than its counterpart built with regular convolution on large-scale recognition tasks such as ImageNet classification.

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.

卷积和自我关注是表示学习的两个强大的技术，通常被认为是两个与彼此不同的对等方法。在本文中，我们表明它们之间存在强烈的潜在关系，从而在这两个范式的大部分计算实际上以相同的操作完成。具体来说，我们首先表明，具有内核大小k x k的传统卷积可以分解为k ^ 2个单独的1x1卷积，然后是换档和求和操作。然后，我们将自我注意模块中的查询，键和值解释为多个1x1卷积，然后计算注意力权重和值的聚合。因此，两个模块的第一阶段包括类似的操作。更重要的是，第一阶段有助于与第二阶段相比的主导计算复杂性（信道大小的正方形）。这种观察结果自然导致这两个看似独特的范例的优雅集成，即享有自我关注和卷积（ACMIX）的益处的混合模型，同时与纯卷积或自我关注对应相比具有最小的计算开销。广泛的实验表明，我们的模型在图像识别和下游任务上持续改进了竞争基础的结果。代码和预先训练的型号将在https://github.com/panxuran/acmix和https://gitee.com/mindspore/models发布。

We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (i.e. shift, scale, and distortion invariance) while maintaining the merits of Transformers (i.e. dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (e.g. ImageNet-22k) and fine-tuned to downstream tasks. Pretrained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks. Code will be released at https: //github.com/leoxiaobin/CvT.

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.

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。

Recent work has shown that self-attention can serve as a basic building block for image recognition models. We explore variations of self-attention and assess their effectiveness for image recognition. We consider two forms of self-attention. One is pairwise self-attention, which generalizes standard dot-product attention and is fundamentally a set operator. The other is patchwise self-attention, which is strictly more powerful than convolution. Our pairwise self-attention networks match or outperform their convolutional counterparts, and the patchwise models substantially outperform the convolutional baselines. We also conduct experiments that probe the robustness of learned representations and conclude that self-attention networks may have significant benefits in terms of robustness and generalization.

人类自然有效地在复杂的场景中找到突出区域。通过这种观察的动机，引入了计算机视觉中的注意力机制，目的是模仿人类视觉系统的这一方面。这种注意机制可以基于输入图像的特征被视为动态权重调整过程。注意机制在许多视觉任务中取得了巨大的成功，包括图像分类，对象检测，语义分割，视频理解，图像生成，3D视觉，多模态任务和自我监督的学习。在本调查中，我们对计算机愿景中的各种关注机制进行了全面的审查，并根据渠道注意，空间关注，暂时关注和分支注意力进行分类。相关的存储库https：//github.com/menghaoguo/awesome-vision-tions致力于收集相关的工作。我们还建议了未来的注意机制研究方向。

Since the recent success of Vision Transformers (ViTs), explorations toward transformer-style architectures have triggered the resurgence of modern ConvNets. In this work, we explore the representation ability of DNNs through the lens of interaction complexities. We empirically show that interaction complexity is an overlooked but essential indicator for visual recognition. Accordingly, a new family of efficient ConvNets, named MogaNet, is presented to pursue informative context mining in pure ConvNet-based models, with preferable complexity-performance trade-offs. In MogaNet, interactions across multiple complexities are facilitated and contextualized by leveraging two specially designed aggregation blocks in both spatial and channel interaction spaces. Extensive studies are conducted on ImageNet classification, COCO object detection, and ADE20K semantic segmentation tasks. The results demonstrate that our MogaNet establishes new state-of-the-art over other popular methods in mainstream scenarios and all model scales. Typically, the lightweight MogaNet-T achieves 80.0\% top-1 accuracy with only 1.44G FLOPs using a refined training setup on ImageNet-1K, surpassing ParC-Net-S by 1.4\% accuracy but saving 59\% (2.04G) FLOPs.

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/ msracver/Deformable-ConvNets.

视觉变压器（VIT）用作强大的视觉模型。与卷积神经网络不同，在前几年主导视觉研究，视觉变压器享有捕获数据中的远程依赖性的能力。尽管如此，任何变压器架构的组成部分，自我关注机制都存在高延迟和低效的内存利用，使其不太适合高分辨率输入图像。为了缓解这些缺点，分层视觉模型在非交错的窗口上局部使用自我关注。这种放松会降低输入尺寸的复杂性;但是，它限制了横窗相互作用，损害了模型性能。在本文中，我们提出了一种新的班次不变的本地注意层，称为查询和参加（QNA），其以重叠的方式聚集在本地输入，非常类似于卷积。 QNA背后的关键想法是介绍学习的查询，这允许快速高效地实现。我们通过将其纳入分层视觉变压器模型来验证我们的层的有效性。我们展示了速度和内存复杂性的改进，同时实现了与最先进的模型的可比准确性。最后，我们的图层尺寸尤其良好，窗口大小，需要高于X10的内存，而不是比现有方法更快。

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to have larger model capacity, their generalization can be worse than convolutional networks due to the lack of the right inductive bias. To effectively combine the strengths from both architectures, we present CoAtNets (pronounced "coat" nets), a family of hybrid models built from two key insights:(1) depthwise Convolution and self-Attention can be naturally unified via simple relative attention; (2) vertically stacking convolution layers and attention layers in a principled way is surprisingly effective in improving generalization, capacity and efficiency. Experiments show that our CoAtNets achieve state-of-the-art performance under different resource constraints across various datasets: Without extra data, CoAtNet achieves 86.0% ImageNet top-1 accuracy; When pre-trained with 13M images from ImageNet-21K, our CoAtNet achieves 88.56% top-1 accuracy, matching ViT-huge pre-trained with 300M images from JFT-300M while using 23x less data; Notably, when we further scale up CoAtNet with JFT-3B, it achieves 90.88% top-1 accuracy on ImageNet, establishing a new state-of-the-art result.1 The initial projection stage can be seen as an aggressive down-sampling convolutional stem.

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。

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of R-CNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.

我们提出了一种多移民通道（MGIC）方法，该方法可以解决参数数量相对于标准卷积神经网络（CNN）中的通道数的二次增长。因此，我们的方法解决了CNN中的冗余，这也被轻量级CNN的成功所揭示。轻巧的CNN可以达到与参数较少的标准CNN的可比精度。但是，权重的数量仍然随CNN的宽度四倍地缩放。我们的MGIC体系结构用MGIC对应物代替了每个CNN块，该块利用了小组大小的嵌套分组卷积的层次结构来解决此问题。因此，我们提出的架构相对于网络的宽度线性扩展，同时保留了通道的完整耦合，如标准CNN中。我们对图像分类，分割和点云分类进行的广泛实验表明，将此策略应用于Resnet和MobilenetV3等不同体系结构，可以减少参数的数量，同时获得相似或更好的准确性。

As a powerful engine, vanilla convolution has promoted huge breakthroughs in various computer tasks. However, it often suffers from sample and content agnostic problems, which limits the representation capacities of the convolutional neural networks (CNNs). In this paper, we for the first time model the scene features as a combination of the local spatial-adaptive parts owned by the individual and the global shift-invariant parts shared to all individuals, and then propose a novel two-branch dual complementary dynamic convolution (DCDC) operator to flexibly deal with these two types of features. The DCDC operator overcomes the limitations of vanilla convolution and most existing dynamic convolutions who capture only spatial-adaptive features, and thus markedly boosts the representation capacities of CNNs. Experiments show that the DCDC operator based ResNets (DCDC-ResNets) significantly outperform vanilla ResNets and most state-of-the-art dynamic convolutional networks on image classification, as well as downstream tasks including object detection, instance and panoptic segmentation tasks, while with lower FLOPs and parameters.

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.

视觉变压器由于能够捕获图像中的长期依赖性的能力而成功地应用于图像识别任务。但是，变压器与现有卷积神经网络（CNN）之间的性能和计算成本仍然存在差距。在本文中，我们旨在解决此问题，并开发一个网络，该网络不仅可以超越规范变压器，而且可以超越高性能卷积模型。我们通过利用变压器来捕获长期依赖性和CNN来建模本地特征，从而提出了一个新的基于变压器的混合网络。此外，我们将其扩展为获得一个称为CMT的模型家族，比以前的基于卷积和基于变压器的模型获得了更好的准确性和效率。特别是，我们的CMT-S在ImageNet上获得了83.5％的TOP-1精度，而在拖鞋上的拖曳率分别比现有的DEIT和EficitiveNet小14倍和2倍。拟议的CMT-S还可以很好地概括CIFAR10（99.2％），CIFAR100（91.7％），花（98.7％）以及其他具有挑战性的视觉数据集，例如可可（44.3％地图），计算成本较小。

与卷积层相比，完全连接的（FC）层更好地在捕获本地模式时更好地建模，但是更糟糕的是，因此通常不对图像识别的青睐。在本文中，我们提出了一种方法，局部注射，通过将培训的并行参数合并到FC内核中的训练参数并将训练的参数合并到FC层中。可以将位置喷射为新颖的结构重新参数化方法，因为它等效地通过转换参数来转换结构。基于此，我们提出了一个名为RepMLP块的多层 - Perceptron（MLP）块，它使用三个FC层提取特征，以及名为Repmlpnet的新颖体系结构。分层设计将RepMLPNET与其他同时提出的视觉MLPS区分开来。由于它生成不同级别的特征映射，它有资格作为下游任务的骨干模型，如语义分割。我们的结果表明，1）地区注射是MLP型号的一般方法; 2）与其他MLP相比，REPMLPNET具有良好的准确性效率折衷; 3）REPMLPNET是第一MLP，可无缝转移到CityCAPES语义分割。代码和模型可在https://github.com/dingxiaoh/repmlp上使用。

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.