We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10× more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https: //github.com/facebookresearch/SlowFast.

在本文中，我们将多尺度视觉变压器（MVIT）作为图像和视频分类的统一架构，以及对象检测。我们提出了一种改进的MVIT版本，它包含分解的相对位置嵌入和残余汇集连接。我们以五种尺寸实例化此架构，并评估Imagenet分类，COCO检测和动力学视频识别，在此优先效果。我们进一步比较了MVITS的汇集注意力来窗口注意力机制，其中它在准确性/计算中优于后者。如果没有钟声，MVIT在3个域中具有最先进的性能：ImageNet分类的准确性为88.8％，Coco对象检测的56.1盒AP和动力学-400视频分类的86.1％。代码和模型将公开可用。

This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. Inspired by feature selection methods in machine learning, a simple stepwise network expansion approach is employed that expands a single axis in each step, such that good accuracy to complexity trade-off is achieved. To expand X3D to a specific target complexity, we perform progressive forward expansion followed by backward contraction. X3D achieves state-of-the-art performance while requiring 4.8× and 5.5× fewer multiply-adds and parameters for similar accuracy as previous work. Our most surprising finding is that networks with high spatiotemporal resolution can perform well, while being extremely light in terms of network width and parameters. We report competitive accuracy at unprecedented efficiency on video classification and detection benchmarks. Code will be available at: https: //github.com/facebookresearch/SlowFast.

We present SlowFast networks for video recognition. Our model involves (i) a Slow pathway, operating at low frame rate, to capture spatial semantics, and (ii) a Fast pathway, operating at high frame rate, to capture motion at fine temporal resolution. The Fast pathway can be made very lightweight by reducing its channel capacity, yet can learn useful temporal information for video recognition. Our models achieve strong performance for both action classification and detection in video, and large improvements are pin-pointed as contributions by our SlowFast concept. We report state-of-the-art accuracy on major video recognition benchmarks, Kinetics, Charades and AVA. Code has been made available at: https://github.com/ facebookresearch/SlowFast.

我们呈现蒙版特征预测（MaskFeat），用于自我监督的视频模型的预训练。我们的方法首先随机地掩盖输入序列的一部分，然后预测蒙面区域的特征。我们研究五种不同类型的功能，找到面向导向渐变（HOG）的直方图，手工制作的特征描述符，在性能和效率方面尤其良好。我们观察到猪中的局部对比标准化对于良好的结果至关重要，这与使用HOG进行视觉识别的早期工作符合。我们的方法可以学习丰富的视觉知识和基于大规模的变压器的模型。在不使用额外的模型重量或监督的情况下，在未标记视频上预先培训的MaskFeat在动力学-400上使用MVIT-L达到86.7％的前所未有的结果，在动力学-600，88.3％上，88.3％，在动力学-700,88.8地图上SSV2上的75.0％。 MaskFeat进一步推广到图像输入，其可以被解释为具有单个帧的视频，并在想象中获得竞争结果。

We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and inference from both inputs. The model is easily scalable and can be adapted to large-scale pre-trained ViTs without requiring full finetuning. The model achieves SOTA results and the code will be open-sourced.

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30x longer than existing models with only 4.5% more compute; traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models are available at https://github.com/facebookresearch/memvit.

我们呈现了基于纯变压器的视频分类模型，在图像分类中最近的近期成功进行了借鉴。我们的模型从输入视频中提取了时空令牌，然后由一系列变压器层编码。为了处理视频中遇到的令牌的长序列，我们提出了我们模型的几种有效的变体，它们将输入的空间和时间维构建。虽然已知基于变换器的模型只有在可用的大型训练数据集时才有效，但我们展示了我们如何在训练期间有效地规范模型，并利用预先训练的图像模型能够在相对小的数据集上训练。我们进行彻底的消融研究，并在包括动力学400和600，史诗厨房，东西的多个视频分类基准上实现最先进的结果，其中 - 基于深度3D卷积网络的现有方法表现出优先的方法。为了促进进一步的研究，我们在https://github.com/google-research/scenic/tree/main/scenic/projects/vivit发布代码

视频理解需要在多种时空分辨率下推理 - 从短的细粒度动作到更长的持续时间。虽然变压器架构最近提出了最先进的，但它们没有明确建模不同的时空分辨率。为此，我们为视频识别（MTV）提供了多视图变压器。我们的模型由单独的编码器组成，表示输入视频的不同视图，以横向连接，以跨视图熔断信息。我们对我们的模型提供了彻底的消融研究，并表明MTV在一系列模型尺寸范围内的准确性和计算成本方面始终如一地表现优于单视对应力。此外，我们在五个标准数据集上实现最先进的结果，并通过大规模预制来进一步提高。我们将释放代码和备用检查点。

在本文中，我们介绍了一种新颖的视觉表示学习，它依赖于少数自适应地学习令牌，并且适用于图像和视频理解任务。而不是依靠手工设计的分割策略来获得视觉令牌并处理大量密集采样的补丁进行关注，我们的方法学会在视觉数据中挖掘重要令牌。这导致有效且有效地找到一些重要的视觉令牌，并且可以在这些令牌之间进行成像注意，在更长的视频的时间范围内，或图像中的空间内容。我们的实验表现出对图像和视频识别任务的几个具有挑战性的基准的强烈性能。重要的是，由于我们的令牌适应性，我们在显着减少的计算金额下实现竞争结果。在计算上更有效的同时，我们获得了对想象成的最先进结果的可比结果。我们在多个视频数据集中建立新的最先进的，包括动力学-400，动力学-600，Charades和Avid。代码可在：https://github.com/google-research/scenic/tree/main/scenic/projects/token_learner

We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures, which are competitive on highly parallel processing hardware. We revisit principles from the extensive literature on convolutional neural networks to apply them to transformers, in particular activation maps with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information in vision transformers.As a result, we propose LeVIT: a hybrid neural network for fast inference image classification. We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. We release the code at https: //github.com/facebookresearch/LeViT.

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.

虽然变形金机对视频识别任务的巨大潜力具有较强的捕获远程依赖性的强大能力，但它们经常遭受通过对视频中大量3D令牌的自我关注操作引起的高计算成本。在本文中，我们提出了一种新的变压器架构，称为双重格式，可以有效且有效地对视频识别进行时空关注。具体而言，我们的Dualformer将完全时空注意力分层到双级级联级别，即首先在附近的3D令牌之间学习细粒度的本地时空交互，然后捕获查询令牌之间的粗粒度全局依赖关系。粗粒度全球金字塔背景。不同于在本地窗口内应用时空分解或限制关注计算以提高效率的现有方法，我们本地 - 全球分层策略可以很好地捕获短期和远程时空依赖项，同时大大减少了钥匙和值的数量在注意计算提高效率。实验结果表明，对抗现有方法的五个视频基准的经济优势。特别是，Dualformer在动态-400/600上设置了新的最先进的82.9％/ 85.2％，大约1000g推理拖鞋，比具有相似性能的现有方法至少3.2倍。

自2020年推出以来，Vision Transformers（VIT）一直在稳步打破许多视觉任务的记录，通常被描述为``全部'''替换Convnet。而且对于嵌入式设备不友好。此外，最近的研究表明，标准的转话如果经过重新设计和培训，可以在准确性和可伸缩性方面与VIT竞争。在本文中，我们采用Convnet的现代化结构来设计一种新的骨干，以采取行动，以采取行动特别是我们的主要目标是为工业产品部署服务，例如仅支持标准操作的FPGA董事会。因此，我们的网络仅由2D卷积组成，而无需使用任何3D卷积，远程注意插件或变压器块。在接受较少的时期（5x-10x）训练时，我们的骨干线超过了（2+1）D和3D卷积的方法，并获得可比的结果s在两个基准数据集上具有vit。

基于变压器的方法最近在基于2D图像的视力任务上取得了巨大进步。但是，对于基于3D视频的任务，例如动作识别，直接将时空变压器应用于视频数据将带来沉重的计算和记忆负担，因为斑块的数量大大增加以及自我注意计算的二次复杂性。如何对视频数据的3D自我注意力进行有效地建模，这对于变压器来说是一个巨大的挑战。在本文中，我们提出了一种时间贴片移动（TPS）方法，用于在变压器中有效的3D自发明建模，以进行基于视频的动作识别。 TPS在时间尺寸中以特定的镶嵌图模式移动斑块的一部分，从而将香草的空间自我发项操作转换为时空的一部分，几乎没有额外的成本。结果，我们可以使用几乎相同的计算和记忆成本来计算3D自我注意力。 TPS是一个插件模块，可以插入现有的2D变压器模型中，以增强时空特征学习。提出的方法可以通过最先进的V1和V1，潜水-48和Kinetics400实现竞争性能，同时在计算和内存成本方面效率更高。 TPS的源代码可在https://github.com/martinxm/tps上找到。

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。

自我关注已成为最近网络架构的一个组成部分，例如，统治主要图像和视频基准的变压器。这是因为自我关注可以灵活地模拟远程信息。出于同样的原因，研究人员最近使尝试恢复多层Perceptron（MLP）并提出一些类似MLP的架构，显示出极大的潜力。然而，当前的MLP样架构不擅长捕获本地细节并缺乏对图像和/或视频中的核心细节的逐步了解。为了克服这个问题，我们提出了一种新颖的Morphmlp架构，该架构专注于在低级层处捕获本地细节，同时逐渐改变，以专注于高级层的长期建模。具体地，我们设计一个完全连接的层，称为Morphfc，两个可变过滤器，其沿着高度和宽度尺寸逐渐地发展其接收领域。更有趣的是，我们建议灵活地调整视频域中的Morphfc层。为了我们最好的知识，我们是第一个创建类似MLP骨干的用于学习视频表示的骨干。最后，我们对图像分类，语义分割和视频分类进行了广泛的实验。我们的Morphmlp，如此自我关注的自由骨干，可以与基于自我关注的型号一样强大。

Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p p×p convolution (p = 16 by default) applied to the input image. This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks. To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3×3 convolutions. While the vast majority of computation in the two ViT designs is identical, we find that this small change in early visual processing results in markedly different training behavior in terms of the sensitivity to optimization settings as well as the final model accuracy. Using a convolutional stem in ViT dramatically increases optimization stability and also improves peak performance (by ∼1-2% top-1 accuracy on ImageNet-1k), while maintaining flops and runtime. The improvement can be observed across the wide spectrum of model complexities (from 1G to 36G flops) and dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us to recommend using a standard, lightweight convolutional stem for ViT models in this regime as a more robust architectural choice compared to the original ViT model design.

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO testdev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-theart by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at https://github. com/microsoft/Swin-Transformer.

Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy of the ViT layers, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose a novel attention operator, called lightweight structure-aware attention (LiSA), which has a better representation power with log-linear complexity. Our operator learns structural patterns by using a set of relative position embeddings (RPEs). To achieve log-linear complexity, the RPEs are approximated with fast Fourier transforms. Our experiments and ablation studies demonstrate that ViTs based on the proposed operator outperform self-attention and other existing operators, achieving state-of-the-art results on ImageNet, and competitive results on other visual understanding benchmarks such as COCO and Something-Something-V2. The source code of our approach will be released online.