对象通常与多个属性相关联，这些属性通常显示出很高的相关性。建模属性之间的复杂关系为多属性学习带来了巨大的挑战。本文提出了一个名为label2label的简单而通用的框架，以利用复杂的属性相关性。 Label2Label是从语言建模的角度来进行多属性预测的首次尝试。具体而言，它将每个属性标签视为描述样本的“单词”。当每个样本带有多个属性标签注释时，这些“单词”自然会形成无序但有意义的“句子”，该句子描述了相应样本的语义信息。受到NLP预训练语言模型的显着成功的启发，Label2Label引入了图像条件的掩盖语言模型，该模型随机掩盖了标签“句子”中的一些“单词”令牌，并旨在基于“蒙版”恢复它们。句子和图像特征传达的上下文。我们的直觉是，如果神经网可以根据上下文和其余属性提示推断丢失的属性，那么实例的属性关系就会得到很好的掌握。 Label2Label在概念上是简单且经验强大的。与高度自定义的特定领域方法相比，我们的方法在不融合特定于任务的先验知识和高度专业的网络设计的情况下，在三个不同的多属性学习任务上获得了最新的结果。代码可从https://github.com/li-wanhua/label2label获得。

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. Firstly, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers.

我们呈现Point-Bert，一种用于学习变压器的新范式，以概括BERT对3D点云的概念。灵感来自BERT，我们将屏蔽点建模（MPM）任务设计为预列火车点云变压器。具体地，我们首先将点云划分为几个本地点修补程序，并且具有离散变化性AutoEncoder（DVAE）的点云标记器被设计为生成包含有意义的本地信息的离散点令牌。然后，我们随机掩盖了一些输入点云的补丁并将它们送入骨干变压器。预训练目标是在销售器获得的点代币的监督下恢复蒙面地点的原始点令牌。广泛的实验表明，拟议的BERT风格的预训练策略显着提高了标准点云变压器的性能。配备了我们的预培训策略，我们表明，纯变压器架构对ModelNet40的准确性为93.8％，在ScanObjectnn的最艰难的设置上的准确性为83.1％，超越精心设计的点云模型，手工制作的设计更少。我们还证明，Point-Bert从新的任务和域中获悉的表示，我们的模型在很大程度上推动了几个射击点云分类任务的最先进。代码和预先训练的型号可在https://github.com/lulutang0608/pint -bert上获得

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.

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

自我监督的预训练技术在文档AI中取得了显着进步。大多数多模式的预训练模型都使用蒙版的语言建模目标来学习文本模式的双向表示，但是它们在图像模式的预训练目标方面有所不同。这种差异增加了多模式表示学习的困难。在本文中，我们建议\ textbf {layoutlmv3}为文档AI预训练多模式变压器，并具有统一的文本和图像掩蔽。此外，LayoutLMV3通过单词斑点对齐目标进行了预训练，可以通过预测是否掩盖文本的相应图像贴片来学习交叉模式对齐。简单的统一体系结构和培训目标使Layoutlmv3成为以文本为中心和以图像为中心的文档AI任务的通用预培训模型。实验结果表明，LayoutLMV3不仅在以文本为中心的任务中实现最先进的绩效，包括形式的理解，收据理解和文档视觉问题回答，而且在以图像为中心的任务（例如文档图像分类和文档布局）中分析。代码和模型可在\ url {https://aka.ms/layoutlmv3}上公开获得。

变压器是一种基于关注的编码器解码器架构，彻底改变了自然语言处理领域。灵感来自这一重大成就，最近在将变形式架构调整到计算机视觉（CV）领域的一些开创性作品，这已经证明了他们对各种简历任务的有效性。依靠竞争力的建模能力，与现代卷积神经网络相比在本文中，我们已经为三百不同的视觉变压器进行了全面的审查，用于三个基本的CV任务（分类，检测和分割），提出了根据其动机，结构和使用情况组织这些方法的分类。 。由于培训设置和面向任务的差异，我们还在不同的配置上进行了评估了这些方法，以便于易于和直观的比较而不是各种基准。此外，我们已经揭示了一系列必不可少的，但可能使变压器能够从众多架构中脱颖而出，例如松弛的高级语义嵌入，以弥合视觉和顺序变压器之间的差距。最后，提出了三个未来的未来研究方向进行进一步投资。

蒙面自动编码在图像和语言领域的自我监督学习方面取得了巨大的成功。但是，基于面具的预处理尚未显示出对点云理解的好处，这可能是由于PointNet（PointNet）无法正确处理训练的标准骨架，而不是通过训练期间掩盖引入的测试分配不匹配。在本文中，我们通过提出一个判别性掩码式变压器框架，maskPoint}来弥合这一差距。我们的关键想法是将点云表示为离散的占用值（1如果点云的一部分；如果不是的，则为0），并在蒙版对象点和采样噪声点之间执行简单的二进制分类作为代理任务。这样，我们的方法是对点云中的点采样差异的强大，并促进了学习丰富的表示。我们在几个下游任务中评估了验证的模型，包括3D形状分类，分割和现实词对象检测，并展示了最新的结果，同时获得了明显的预读速度（例如，扫描仪上的4.1倍）先前的最新变压器基线。代码可在https://github.com/haotian-liu/maskpoint上找到。

Although various methods have been proposed for multi-label classification, most approaches still follow the feature learning mechanism of the single-label (multi-class) classification, namely, learning a shared image feature to classify multiple labels. However, we find this One-shared-Feature-for-Multiple-Labels (OFML) mechanism is not conducive to learning discriminative label features and makes the model non-robustness. For the first time, we mathematically prove that the inferiority of the OFML mechanism is that the optimal learned image feature cannot maintain high similarities with multiple classifiers simultaneously in the context of minimizing cross-entropy loss. To address the limitations of the OFML mechanism, we introduce the One-specific-Feature-for-One-Label (OFOL) mechanism and propose a novel disentangled label feature learning (DLFL) framework to learn a disentangled representation for each label. The specificity of the framework lies in a feature disentangle module, which contains learnable semantic queries and a Semantic Spatial Cross-Attention (SSCA) module. Specifically, learnable semantic queries maintain semantic consistency between different images of the same label. The SSCA module localizes the label-related spatial regions and aggregates located region features into the corresponding label feature to achieve feature disentanglement. We achieve state-of-the-art performance on eight datasets of three tasks, \ie, multi-label classification, pedestrian attribute recognition, and continual multi-label learning.

Joint image-text embedding is the bedrock for most Visionand-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage finegrained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OTbased WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question

连接视觉和语言在生成智能中起着重要作用。因此，已经致力于图像标题的大型研究工作，即用句法和语义有意义的句子描述图像。从2015年开始，该任务通常通过由Visual Encoder组成的管道和文本生成的语言模型来解决任务。在这些年来，两种组件通过对象区域，属性，介绍多模态连接，完全关注方法和伯特早期融合策略的利用而显着发展。但是，无论令人印象深刻的结果，图像标题的研究还没有达到结论性答案。这项工作旨在提供图像标题方法的全面概述，从视觉编码和文本生成到培训策略，数据集和评估度量。在这方面，我们量化地比较了许多相关的最先进的方法来确定架构和培训策略中最有影响力的技术创新。此外，讨论了问题的许多变体及其开放挑战。这项工作的最终目标是作为理解现有文献的工具，并突出显示计算机视觉和自然语言处理的研究领域的未来方向可以找到最佳的协同作用。

Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on representations, wherein the attention maps of different layers are learned separately without explicit interactions. In this paper, we propose a novel and generic evolving attention mechanism, which directly models the evolution of inter-token relationships through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at https://github.com/pkuyym/EvolvingAttention

事实证明，大规模的视觉和语言（V+L）预训练已被证明有效地增强了下游V+L任务。但是，当涉及时尚域时，现有的V+L方法是不足的，因为它们忽略了时尚V+L数据和下游任务的独特特征。在这项工作中，我们提出了一个以时尚为中心的新型V+L表示框架，被称为Fashionvil。它包含两个新型时尚特定的预训练任务，旨在使用时尚V+L数据利用两个内在属性。首先，与其他域仅包含单个图像文本对的其他域相比，时尚域中可能有多个图像。因此，我们提出了一项多视图对比学习任务，以将一个图像的可视化表示为另一个图像+文本的组成多模式表示。其次，时尚文本（例如，产品描述）通常包含丰富的细粒概念（属性/名词短语）。为了利用这一点，引入了伪归因于分类任务，以鼓励同一概念的学习的单峰（视觉/文本）表示。此外，时尚V+L任务唯一包含不符合常见的一流或两流体系结构的任务（例如，文本引导的图像检索）。因此，我们提出了一个灵活的，多功能的V+L模型体系结构，该体系结构由模态 - 静态变压器组成，以便可以灵活地适应任何下游任务。广泛的实验表明，我们的FashionVil在五个下游任务中实现了新的最新技术。代码可从https://github.com/brandonhanx/mmf获得。

文本识别是文档数字化的长期研究问题。现有的方法通常是基于CNN构建的，以用于图像理解，并为Char-Level文本生成而建立RNN。此外，通常需要另一种语言模型来提高整体准确性作为后处理步骤。在本文中，我们提出了一种使用预训练的图像变压器和文本变压器模型（即Trocr）提出的端到端文本识别方法，该模型利用了变压器体系结构，以实现图像理解和文字级级文本生成。TROR模型很简单，但有效，可以通过大规模合成数据进行预训练，并通过人体标记的数据集进行微调。实验表明，TROR模型的表现优于印刷，手写和场景文本识别任务上的当前最新模型。Trocr模型和代码可在\ url {https://aka.ms/trocr}上公开获得。

在培训数据中拟合复杂的模式，例如推理和争议，是语言预训练的关键挑战。根据最近的研究和我们的经验观察，一种可能的原因是训练数据中的一些易于适应的模式，例如经常共同发生的单词组合，主导和伤害预训练，使模型很难适合更复杂的信息。我们争辩说，错误预测可以帮助找到危害语言理解的这种主导模式。当发生错误预测时，应该经常与导致MIS预测的模型拟合的MIS预测字相同的模式。如果我们可以添加正规化以培训模型，当MIS预测发生并更多地对待更微妙的模式时，可以在更多信息上缩小到这种主导模式时，可以在预训练中有效地安装更多信息。在此动机之后，我们提出了一种新的语言预培训方法，错误预测作为伤害警报（MPA）。在MPA中，当在预训练期间发生错误预测时，我们使用其共同发生信息来指导自我关注模块的多个头部。变压器模块中的一些自我关注头经过优化，以将更低的注意重量分配给频繁地在误报中的输入句子中的单词，同时将更高权重分配给另一个单词。通过这样做，变压器模型训练，以依赖于主导的频繁共同发生模式，而在误报中，当发生错误预测时，在剩余更复杂的信息上更加关注更多。我们的实验表明，MPA加快了伯特和电器的预训练，并提高了他们对下游任务的表现。

We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based visual features usually represent parts of an image, it is challenging for existing visionlanguage models to fully understand the semantics from paired natural languages. In this paper, we propose SOHO to "See Out of tHe bOx" that takes a whole image as input, and learns vision-language representation in an endto-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than regionbased approaches. In particular, SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in our proposed pre-training task Masked Visual Modeling (MVM). We conduct experiments on four well-established vision-language tasks by following standard VLPT settings. In particular, SOHO achieves absolute gains of 2.0% R@1 score on MSCOCO text retrieval 5k test split, 1.5% accuracy on NLVR 2 test-P split, 6.7% accuracy on SNLI-VE test split, respectively.

基于变压器的自我监督表示方法学习方法从未标记的数据集中学习通用功能，以提供有用的网络初始化参数，用于下游任务。最近，基于掩盖3D点云数据的局部表面斑块的自我监督学习的探索还不足。在本文中，我们提出了3D点云表示学习中的蒙版自动编码器（缩写为MAE3D），这是一种新颖的自动编码范式，用于自我监督学习。我们首先将输入点云拆分为补丁，然后掩盖其中的一部分，然后使用我们的补丁嵌入模块提取未掩盖的补丁的功能。其次，我们采用贴片的MAE3D变形金刚学习点云补丁的本地功能以及补丁之间的高级上下文关系，并完成蒙版补丁的潜在表示。我们将点云重建模块与多任务损失一起完成，从而完成不完整的点云。我们在Shapenet55上进行了自我监督的预训练，并使用点云完成前文本任务，并在ModelNet40和ScanObjectnn（PB \ _t50 \ _RS，最难的变体）上微调预训练的模型。全面的实验表明，我们的MAE3D从Point Cloud补丁提取的本地功能对下游分类任务有益，表现优于最先进的方法（$ 93.4 \％\％\％\％$和$ 86.2 \％$ $分类精度）。

我们提出了引导蒙面的自动编码器（bootmae），这是一种新的视觉BERT预训练方法。 Bootmae用两个核心设计改进了原始的蒙版自动编码器（MAE）：1）动量编码器，该动量编码器可作为额外的BERT预测目标提供在线功能； 2）试图降低编码器的压力以记住目标特定信息的靶向解码器。第一个设计的动机是通过观察到的，即使用预定的MAE提取特征，因为掩盖令牌的BERT预测目标可以实现更好的预训练性能。因此，我们与原始的MAE编码器并行添加了一个动量编码器，该编码器通过将其自己的表示作为BERT预测目标来引导预处理性能。在第二个设计中，我们将特定于目标的信息（例如，未掩盖贴片的像素值）直接传达到解码器中，以减少记住目标特定信息的编码器的压力。因此，编码器专注于语义建模，这是BERT预训练的目的，并且不需要浪费其在记住与预测目标相关的未掩盖令牌的信息时的能力。通过广泛的实验，我们的Bootmae在ImageNet-1k上获得了$ 84.2 \％$ $ $ $+0.8 \％$在同一预训练时期。 Bootmae还获得了$+1.0 $ MIOU在ADE20K上的语义细分和$+1.3 $ box ap，$+1.4 $+1.4 $ bask ap改进对象检测和可可数据集上的细分。代码在https://github.com/lightdxy/bootmae上发布。

计算机视觉任务可以从估计突出物区域和这些对象区域之间的相互作用中受益。识别对象区域涉及利用预借鉴模型来执行对象检测，对象分割和/或对象姿势估计。但是，由于以下原因，在实践中不可行：1）预用模型的训练数据集的对象类别可能不会涵盖一般计算机视觉任务的所有对象类别，2）佩戴型模型训练数据集之间的域间隙并且目标任务的数据集可能会影响性能，3）预磨模模型中存在的偏差和方差可能泄漏到导致无意中偏置的目标模型的目标任务中。为了克服这些缺点，我们建议利用一系列视频帧捕获一组公共对象和它们之间的相互作用的公共基本原理，因此视频帧特征之间的共分割的概念可以用自动的能力装配模型专注于突出区域，以最终的方式提高潜在的任务的性能。在这方面，我们提出了一种称为“共分割激活模块”（COSAM）的通用模块，其可以被插入任何CNN，以促进基于CNN的任何CNN的概念在一系列视频帧特征中的关注。我们在三个基于视频的任务中展示Cosam的应用即1）基于视频的人Re-ID，2）视频字幕分类，并证明COSAM能够在视频帧中捕获突出区域，从而引导对于显着的性能改进以及可解释的关注图。

有效的视频识别是一个热点研究主题，具有互联网和移动设备上多媒体数据的爆炸性增长。大多数现有方法都选择了显着帧，而不意识对特定于类的显着性分数，这忽略了框架显着性及其归属类别之间的隐式关联。为了减轻此问题，我们设计了一种新颖的时间显着性查询（TSQ）机制，该机制引入了特定于类的信息，以提供明显测量的细粒线索。具体而言，我们将特定于类的显着性测量过程建模为查询响应任务。对于每个类别，它的共同模式被用作查询，最突出的框架对其进行了响应。然后，计算出的相似性被用作框架显着性得分。为了实现这一目标，我们提出了一个时间显着性查询网络（TSQNET），其中包括基于视觉外观相似性和文本事件对象关系的TSQ机制的两个实例化。之后，实施了交叉模式相互作用以促进它们之间的信息交换。最后，我们使用了两种模式生成的最自信类别的特定阶级销售，以执行显着框架的选择。广泛的实验通过在ActivityNet，FCVID和Mini-Kinetics数据集上实现最新结果来证明我们方法的有效性。我们的项目页面位于https://lawrencexia2008.github.io/projects/tsqnet。