Vision Transformer (ViT) extracts the final representation from either class token or an average of all patch tokens, following the architecture of Transformer in Natural Language Processing (NLP) or Convolutional Neural Networks (CNNs) in computer vision. However, studies for the best way of aggregating the patch tokens are still limited to average pooling, while widely-used pooling strategies, such as max and GeM pooling, can be considered. Despite their effectiveness, the existing pooling strategies do not consider the architecture of ViT and the channel-wise difference in the activation maps, aggregating the crucial and trivial channels with the same importance. In this paper, we present Group Generalized Mean (GGeM) pooling as a simple yet powerful pooling strategy for ViT. GGeM divides the channels into groups and computes GeM pooling with a shared pooling parameter per group. As ViT groups the channels via a multi-head attention mechanism, grouping the channels by GGeM leads to lower head-wise dependence while amplifying important channels on the activation maps. Exploiting GGeM shows 0.1%p to 0.7%p performance boosts compared to the baselines and achieves state-of-the-art performance for ViT-Base and ViT-Large models in ImageNet-1K classification task. Moreover, GGeM outperforms the existing pooling strategies on image retrieval and multi-modal representation learning tasks, demonstrating the superiority of GGeM for a variety of tasks. GGeM is a simple algorithm in that only a few lines of code are necessary for implementation.

变形金刚和蒙版语言建模在计算机视觉中很快被视为视觉变压器和蒙版图像建模（MIM）。在这项工作中，我们认为由于图像中令牌的数量和相关性，图像令牌掩盖与文本中的令牌掩盖有所不同。特别是，为了为MIM产生具有挑战性的借口任务，我们主张从随机掩盖到知情掩盖的转变。我们在基于蒸馏的MIM的背景下开发并展示了这一想法，其中教师变压器编码器生成了一个注意力图，我们用它来指导学生为学生指导掩盖。因此，我们引入了一种新颖的掩蔽策略，称为注意引导蒙版（ATTMASK），我们证明了其对基于密集蒸馏的MIM以及基于普通蒸馏的自然剥离的自助力学习的有效性。我们确认ATTMASK可以加快学习过程，并提高各种下游任务的性能。我们在https://github.com/gkakogeorgiou/attmask上提供实现代码。

语言变形金刚的成功主要归因于屏蔽语言建模（MLM）的借口任务，其中文本首先被致以语义有意义的作品。在这项工作中，我们研究了蒙面图像建模（MIM），并指出使用语义有意义的视觉销售器的优缺点。我们提出了一个自我监督的框架IBOT，可以使用在线标记器执行蒙版预测。具体而言，我们在蒙面的补丁令牌上进行自我蒸馏，并将教师网络作为在线标记器，以及在课堂上的自蒸馏来获取视觉语义。在线销售器与MIM目标和分配的多级培训管道共同学习，销售器需要预先预先培训。通过在Imagenet-1K上达到81.6％的线性探测精度和86.3％的微调精度来展示IBOT的突出。除了最先进的图像分类结果之外，我们强调了新兴的局部语义模式，这有助于模型对共同损坏获得强大的鲁棒性，并在密集的下游任务中实现领先的结果，例如，对象检测，实例分割和语义细分。

There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial relevance and diverse channel representation. First, on spatial aspect, objects are locally compact and relevant, thus fine-grained feature needs to be extracted from a token and its neighbors. While the lack of data hinders ViTs to attend the spatial relevance. Second, on channel aspect, representation exhibits diversity on different channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition. To this end, we propose Dynamic Hybrid Vision Transformer (DHVT) as the solution to enhance the two inductive biases. On spatial aspect, we adopt a hybrid structure, in which convolution is integrated into patch embedding and multi-layer perceptron module, forcing the model to capture the token features as well as their neighboring features. On channel aspect, we introduce a dynamic feature aggregation module in MLP and a brand new "head token" design in multi-head self-attention module to help re-calibrate channel representation and make different channel group representation interacts with each other. The fusion of weak channel representation forms a strong enough representation for classification. With this design, we successfully eliminate the performance gap between CNNs and ViTs, and our DHVT achieves a series of state-of-the-art performance with a lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on ImageNet-1K with 24.0M parameters. Code is available at https://github.com/ArieSeirack/DHVT.

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train. 1

大规模的多模式对比预训练已经证明了通过将多种模式映射到共享嵌入空间中的一系列下游任务的可转移功能。通常，这对每种模式都采用了单独的编码器。但是，最近的工作表明，变形金刚可以支持跨多种方式学习并允许知识共享。受此启发，我们研究了各种模式共享的对比语言图像预训练（MS-CLIP）框架。更具体地说，我们质疑在对比预训练期间可以在跨模态共享变压器模型的多少个参数，并严格检查建筑设计选择，以将沿频谱共享的参数比例定位。在研究的条件下，我们观察到，视觉和语言信号的主要统一编码器优于所有其他分离更多参数的变体。此外，我们发现特定于特定于模态的平行模块进一步提高了性能。实验结果表明，所提出的MS-CLIP方法在零摄像机分类中（在YFCC-100M上进行了预训练）中，最多可超过13 \％相对的香草夹，同时支持降低参数。此外，在24个下游视觉任务的集合中，我们的方法在线性探测中优于Vanilla剪辑。此外，我们发现共享参数导致语义概念来自不同方式在嵌入空间中更接近地编码，从而促进了共同的语义结构（例如注意力模式）从语言到视觉的传递。代码可在\ href {https://github.com/hxyou/msclip} {url}中获得。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision tasks as being an alternative architecture against the existing convolutional neural networks (CNN). Since the transformer-based architecture has been innovative for computer vision modeling, the design convention towards an effective architecture has been less studied yet. From the successful design principles of CNN, we investigate the role of spatial dimension conversion and its effectiveness on transformer-based architecture. We particularly attend to the dimension reduction principle of CNNs; as the depth increases, a conventional CNN increases channel dimension and decreases spatial dimensions. We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model. We show that PiT achieves the improved model capability and generalization performance against ViT. Throughout the extensive experiments, we further show PiT outperforms the baseline on several tasks such as image classification, object detection, and robustness evaluation. Source codes and ImageNet models are available at https://github.com/naver-ai/pit.

我们展示了如何通过基于关注的全球地图扩充任何卷积网络，以实现非本地推理。我们通过基于关注的聚合层替换为单个变压器块的最终平均池，重量贴片如何参与分类决策。我们使用2个参数（宽度和深度）使用简单的补丁卷积网络，使用简单的补丁的卷积网络插入学习的聚合层。与金字塔设计相比，该架构系列在所有层上维护输入补丁分辨率。它在准确性和复杂性之间产生了令人惊讶的竞争权衡，特别是在记忆消耗方面，如我们在各种计算机视觉任务所示：对象分类，图像分割和检测的实验所示。

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.

Intermediate features of a pre-trained model have been shown informative for making accurate predictions on downstream tasks, even if the model backbone is kept frozen. The key challenge is how to utilize these intermediate features given their gigantic amount. We propose visual query tuning (VQT), a simple yet effective approach to aggregate intermediate features of Vision Transformers. Through introducing a handful of learnable ``query'' tokens to each layer, VQT leverages the inner workings of Transformers to ``summarize'' rich intermediate features of each layer, which can then be used to train the prediction heads of downstream tasks. As VQT keeps the intermediate features intact and only learns to combine them, it enjoys memory efficiency in training, compared to many other parameter-efficient fine-tuning approaches that learn to adapt features and need back-propagation through the entire backbone. This also suggests the complementary role between VQT and those approaches in transfer learning. Empirically, VQT consistently surpasses the state-of-the-art approach that utilizes intermediate features for transfer learning and outperforms full fine-tuning in many cases. Compared to parameter-efficient approaches that adapt features, VQT achieves much higher accuracy under memory constraints. Most importantly, VQT is compatible with these approaches to attain even higher accuracy, making it a simple add-on to further boost transfer learning.

我们引入了一个自我监督的视觉表示模型BEIT，该模型代表来自图像变压器的双向编码器表示。在Bert在自然语言处理区域中开发后，我们提出了一项掩盖的图像建模任务，以预识视觉变压器。具体而言，每个图像在我们的预训练中具有两个视图，即图像贴片（例如16x16像素）和视觉令牌（即离散令牌）。我们首先将原始图像“将”“令牌化”到视觉令牌中。然后，我们随机掩盖了一些图像补丁并将其喂入骨干变压器中。预训练的目标是根据损坏的图像补丁恢复原始的视觉令牌。在预训练BEIT之后，我们通过将任务层附加在预审计的编码器上，直接通过将任务层附加到下游任务上的模型参数。图像分类和语义分割的实验结果表明，我们的模型通过以前的预训练方法实现了竞争结果。例如，基本大小的BEIT在Imagenet-1K上获得了83.2％的TOP-1精度，并以相同的设置优于划痕DEIT训练（81.8％）。此外，大尺寸的BEIT仅使用Imagenet-1K获得86.3％，即使在Imagenet-22K上进行预训练（85.2％），甚至超过了VIT-L。代码和预估计的模型可在https://aka.ms/beit上找到。

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

本文探讨了贝尔视觉变压器预训练的更好的码本。最近的工作成功地转移了从NLP到视野领域的BERT预训练。它直接采用一个简单的离散VAE作为视觉销售器，但尚未考虑由此产生的视觉令牌的语义水平。相比之下，NLP字段中的离散令牌是自然的高度语义。这种差异激励我们学习一个感知码本。我们惊奇地找到了一个简单而有效的想法：在DVAE训练期间强制执行感知相似性。我们证明，所提出的感知码本生成的视觉令牌确实表现出更好的语义含义，随后有助于预训练在各种下游任务中实现卓越的转移性能。例如，我们在Imagenet-1K上实现了84.5前1个精度，vit-B骨干，优于竞争方法Beit +1.3，具有相同的训练纪元。它还可以通过+1.3框AP和+1.0掩模AP，在ADE20K上的语义细分，在ADE20K上提高对象检测和分割任务的性能，+1.0 miou，代码和型号将在\ url {https：// github.com/microsoft/peco}。

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16×16) as "visual sentences" and present to further divide them into smaller patches (e.g., 4×4) as "visual words". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, e.g., we achieve an 81.5% top-1 accuracy on the ImageNet, which is about 1.7% higher than that of the state-of-the-art visual transformer with similar computational cost.

最近，自我监督的蒙面自动编码器（MAE）因其令人印象深刻的表示能力而引起了前所未有的关注。但是，借口任务是掩盖的图像建模（MIM），重建缺失的本地贴片，缺乏对图像的全局理解。本文通过添加有监督的分类部门将MAE扩展到了完全监督的环境，从而使Mae可以从Golden Labels中有效地学习全球功能。所提出的监督MAE（Supmae）仅利用图像贴片的可见子集进行分类，这与使用所有图像贴片的标准监督预训练不同。通过实验，我们证明了Supmae不仅更有效地训练，而且还学会了更健壮和可转移的功能。具体而言，Supmae在使用VIT-B/16模型的ImageNet上评估时仅使用30％的计算来实现MAE的可比性。 Supmae对ImageNet变体的鲁棒性和转移学习绩效优于MAE和标准监督前培训对手。代码将公开可用。

本文显示屏蔽的自动化器（MAE）是可扩展的自我监督学习者，用于计算机愿景。我们的MAE方法很简单：我们掩盖输入图像的随机补丁并重建缺失像素。它基于两个核心设计。首先，我们开发一个不对称的编码器解码器架构，其中编码器仅在掩码的可见子集（没有掩码令牌）上，以及重量解码器，该重量解码器从潜像和掩码令牌重建原始图像。其次，我们发现掩蔽了高比例的输入图像，例如，75％，产生非凡和有意义的自我监督任务。耦合这两种设计使我们能够有效且有效地培训大型模型：我们加速培训（3倍或更多）并提高准确性。我们可扩展的方法允许学习概括的高容量模型：例如，Vanilla Vit-Maxim模型在使用Imagenet-1K数据的方法中实现最佳准确性（87.8％）。下游任务中的转移性能优于监督预培训并显示有前途的缩放行为。

Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adversely affect the accuracy of CLIP-based training. We hypothesize that removing a large portion of image tokens may improperly discard the semantic content associated with a given text description, thus constituting an incorrect pairing target in CLIP training. To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description. The correlation scores are computed in an online fashion using the EMA version of the visual encoder. Our experiments show that the proposed attentive masking approach performs better than the previous method of random token removal for CLIP training. The approach also makes it efficient to apply multiple augmentation views to the image, as well as introducing instance contrastive learning tasks between these views into the CLIP framework. Compared to other CLIP improvements that combine different pre-training targets such as SLIP and MaskCLIP, our method is not only more effective, but also much more efficient. Specifically, using ViT-B and YFCC-15M dataset, our approach achieves $43.9\%$ top-1 accuracy on ImageNet-1K zero-shot classification, as well as $62.7/42.1$ and $38.0/23.2$ I2T/T2I retrieval accuracy on Flickr30K and MS COCO, which are $+1.1\%$, $+5.5/+0.9$, and $+4.4/+1.3$ higher than the SLIP method, while being $2.30\times$ faster. An efficient version of our approach running $1.16\times$ faster than the plain CLIP model achieves significant gains of $+5.3\%$, $+11.3/+8.0$, and $+9.5/+4.9$ on these benchmarks.