We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation and semantic segmentation without heavy supervised training. Moreover, we observe quantitative changes in scaling EVA result in qualitative changes in transfer learning performance that are not present in other models. For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVISv1.0 dataset with over a thousand categories and COCO dataset with only eighty categories. Beyond a pure vision encoder, EVA can also serve as a vision-centric, multi-modal pivot to connect images and text. We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation models. To facilitate future research, we release all the code and models at https://github.com/baaivision/EVA.

语言，视觉和多模式预审查的大量融合正在出现。在这项工作中，我们介绍了通用多模式基础模型BEIT-3，该模型BEIT-3，该模型在视觉和视觉任务上都实现了最新的转移性能。具体来说，我们从三个方面提出了大融合：骨干架构，预训练任务和模型扩展。我们介绍了多道路变压器进行通用建模，其中模块化体系结构可以实现深融合和模态特定的编码。基于共享的骨干，我们以统一的方式对图像（Imglish），文本（英语）和图像文本对（“平行句子”）进行蒙面的“语言”建模。实验结果表明，BEIT-3在对象检测（COCO），语义分割（ADE20K），图像分类（Imagenet），视觉推理（NLVR2），视觉询问答案（VQAV2），图像字幕上获得最先进的性能（可可）和跨模式检索（Flickr30k，可可）。

自动视觉解对我们多样化和开放的世界需要计算机视觉模型，以概括为特定任务的最小定制，类似于人类视力。计算机视觉基础型号培训，培训多样化，大型数据集，可以适应各种下游任务，对该任务来解决现实世界计算机视觉应用而言至关重要。虽然现有的视觉基础模型如剪辑，对齐和吴道2.0主要集中在映射图像和文本表示到跨模型共享表示，我们介绍了一台新的计算机视觉基础模型，佛罗伦萨，扩大粗糙的表示（现场）到精细（对象），从静态（图像）到动态（视频），以及从RGB到多个模态（标题，深度）。通过从Web级图像文本数据中纳入通用视觉语言表示，我们的佛罗伦萨模型可以很容易地适应各种计算机视觉任务，例如分类，检索，对象检测，VQA，图像标题，视频检索和动作识别。此外，佛罗伦萨在许多类型的转移学习中表现出出色的表现：全面采样的微调，线性探测，几次射击传输和用于新颖图像和物体的零拍摄传输。所有这些属性对于我们的视觉基础模型至关重要，以提供通用视觉任务。佛罗伦萨实现了新的最先进的导致44个代表性基准，例如Imagenet-1K零射击分类，最高1精度为83.74，最高5个精度为97.18，62.4地图上的Coco微调， 80.36在VQA上，动力学-600上的87.8。

Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output format and the type of information that is of value. In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition. From this empirical analysis, our work answers the questions of what pretraining task fits best with this frozen setting, how to make the frozen setting more flexible to various downstream tasks, and the effect of larger model sizes. We additionally examine the upper bound of performance using a giant frozen pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches competitive performance on a varied set of major benchmarks with only one shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7 top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to bring greater attention to this promising path of freezing pretrained image models.

本文提出了一个简单而有效的框架蒙版，该框架将新提出的掩盖自distillation纳入对比的语言图像预处理中。掩盖自distillation的核心思想是将表示从完整的图像提取到蒙版图像预测的表示形式。这种合并享有两个重要的好处。首先，掩盖的自我验证目标是本地贴片表示学习，这与视觉对比度的互补，专注于与文本相关的表示。二，掩盖的自我验证也与视觉语言对比符合训练目标的视野对比是一致的。视觉编码器用于功能对齐，因此能够学习本地语义从该语言中获得间接监督。我们提供了专门设计的实验，并进行了全面的分析，以验证这两个好处。从经验上讲，我们表明，当MaskClip应用于各种具有挑战性的下游任务时，可以在线性探测，填充和零拍摄中取得卓越的结果，并在语言编码器的指导下取得了卓越的结果。

最近的进展表明，使用对比图像文本对的大规模预训练可以是从自然语言监督的高质量视觉表演学习的有前途的替代方案。从更广泛的监督来源受益，这种新的范例展示了对下游分类任务和数据集的令人印象深刻的可转移性。然而，从图像文本对中学习的知识转移到更复杂的密集预测任务的问题几乎没有访问过。在这项工作中，我们通过隐式和明确地利用来自剪辑的预先训练的知识来提出了一种新的密集预测框架。具体地，我们将剪辑中的原始图像文本匹配问题转换为像素文本匹配问题，并使用像素文本分数图来指导致密预测模型的学习。通过进一步使用图像中的上下文信息来提示语言模型，我们能够促进我们的模型来更好地利用预先接受训练的知识。我们的方法是模型 - 不可行的，它可以应用于任意密集的预测系统和各种预先训练的视觉底座，包括夹模型和想象成预先训练的模型。广泛的实验证明了我们对语义分割，对象检测和实例分段任务的方法的卓越性能。代码可在https://github.com/raoyongming/denseclip获得

在过去的几年中，基于自我注意力的变压器模型一直在主导许多计算机视觉任务。它们的出色模型质量在很大程度上取决于标记过多的图像数据集。为了减少对大型标记数据集的依赖，基于重建的掩盖自动编码器正在获得流行，这些自动编码器从未标记的图像中学习了高质量的可转移表示形式。出于同样的目的，最近弱监督的图像预处理方法探索了图像随附的文本字幕的语言监督。在这项工作中，我们提出了对语言辅助代表的预读图像，称为米兰。我们的预处理目标不是预测原始像素或低级别的特征，而是用使用字幕监督获得的大量语义信号来重建图像特征。此外，为了适应我们的重建目标，我们提出了更有效的促使解码器体系结构和语义意识到的掩码采样机制，从而进一步推进了预告片模型的传输性能。实验结果表明，米兰的精度比以前的工作更高。当掩盖的自动编码器在ImagEnet-1K数据集上进行了预估计并以224x224的输入分辨率进行了填充时，米兰在VITB/16上的前1位准确性达到了85.4％，使以前的先前最先前的艺术品达到1％。在下游的语义分割任务中，米兰在ADE20K数据集上使用VIT-B/16骨架达到52.7 MIOU，表现优于先前的蒙版预读结果4分。

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。

Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previous work on scaling laws has primarily used private data \& models or focused on uni-modal language or vision learning. To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository. Our large-scale experiments involve models trained on up to two billion image-text pairs and identify power law scaling for multiple downstream tasks including zero-shot classification, retrieval, linear probing, and end-to-end fine-tuning. We find that the training distribution plays a key role in scaling laws as the OpenAI and OpenCLIP models exhibit different scaling behavior despite identical model architectures and similar training recipes. We open-source our evaluation workflow and all models, including the largest public CLIP models, to ensure reproducibility and make scaling laws research more accessible. Source code and instructions to reproduce this study will be available at https://github.com/LAION-AI/scaling-laws-openclip

The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on image-level pretraining and adpation, which are limited for dynamic and complex video-level understanding tasks. To fill the gap, we present general video foundation models, InternVideo, by taking advantage of both generative and discriminative self-supervised video learning. Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications. Without bells and whistles, InternVideo achieves state-of-the-art performance on 39 video datasets from extensive tasks including video action recognition/detection, video-language alignment, and open-world video applications. Especially, our methods can obtain 91.1% and 77.2% top-1 accuracy on the challenging Kinetics-400 and Something-Something V2 benchmarks, respectively. All of these results effectively show the generality of our InternVideo for video understanding. The code will be released at https://github.com/OpenGVLab/InternVideo .

将简单的体系结构与大规模预训练相结合已导致图像分类的大量改进。对于对象检测，预训练和缩放方法的确定性不佳，尤其是在长尾和开放式摄影的环境中，训练数据相对较少。在本文中，我们提出了一个强大的配方，用于将图像文本模型转移到开放式对象检测中。我们使用具有最小修改，对比度文本预训练和端到端检测微调的标准视觉变压器体系结构。我们对该设置的缩放属性的分析表明，增加图像级预训练和模型大小在下游检测任务上产生一致的改进。我们提供适应性策略和正规化，以实现零击文本条件和单次图像条件对象检测的非常强劲的性能。代码和型号可在GitHub上找到。

蒙面自动编码器已成为自我监督的视觉表示学习的流行培训范例。这些模型随机掩盖了输入的一部分，并根据目标表示形式重建蒙版部分。在本文中，我们首先表明，对目标表示的仔细选择对于学习良好表示形式不必要，因为不同的目标倾向于得出相似的模型。在这一观察结果的驱动下，我们提出了一个多阶段掩盖的蒸馏管道，并使用随机初始化的模型作为教师，使我们能够有效地训练高容量模型，而无需仔细设计目标表示形式。有趣的是，我们进一步探索了能力较大的教师，获得具有出色转移能力的蒸馏学生。在分类，转移学习，对象检测和语义分割的不同任务上，使用自举的教师（DBOT）执行掩盖知识蒸馏的建议方法优于先前的自我监督方法，而不是非平凡的边缘。我们希望我们的发现以及拟议的方法能够激励人们重新考虑目标表征在预训练的蒙面自动编码器中的作用。

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。

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning.

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。

The combination of transformers and masked image modeling (MIM) pre-training framework has shown great potential in various vision tasks. However, the pre-training computational budget is too heavy and withholds the MIM from becoming a practical training paradigm. This paper presents FastMIM, a simple and generic framework for expediting masked image modeling with the following two steps: (i) pre-training vision backbones with low-resolution input images; and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images. In addition, we propose FastMIM-P to progressively enlarge the input resolution during pre-training stage to further enhance the transfer results of models with high capacity. We point out that: (i) a wide range of input resolutions in pre-training phase can lead to similar performances in fine-tuning phase and downstream tasks such as detection and segmentation; (ii) the shallow layers of encoder are more important during pre-training and discarding last several layers can speed up the training stage with no harm to fine-tuning performance; (iii) the decoder should match the size of selected network; and (iv) HOG is more stable than RGB values when resolution transfers;. Equipped with FastMIM, all kinds of vision backbones can be pre-trained in an efficient way. For example, we can achieve 83.8%/84.1% top-1 accuracy on ImageNet-1K with ViT-B/Swin-B as backbones. Compared to previous relevant approaches, we can achieve comparable or better top-1 accuracy while accelerate the training procedure by $\sim$5$\times$. Code can be found in https://github.com/ggjy/FastMIM.pytorch.

探索大规模预处理的基础模型对计算机视觉具有重大兴趣，因为这些模型可以快速转移到许多下游任务中。本文介绍了对比字幕（COCA），这是一种极简主义的设计，旨在为图像文本编码器编码器基础模型预算与对比度损失和字幕损失，从而从剪辑和诸如simvlm之类的生成方法之类的对比方法中包含模型能力。与所有解码器层都参与编码器输出的标准编码器 - 模块变压器相反，可口可乐省略了解码器层的上半部分的交叉注意，以编码单峰文本表示，并串联到剩余的解码器层，这些解码器与图像编码器相交的解码器层多模式图像文本表示。除了对多模态解码器输出的字幕损失外，我们还应用了单峰图像和文本嵌入之间的对比损失，该输出可以预测文本令牌自动加压。通过共享相同的计算图，可以用最小的开销有效地计算两个培训目标。可口可乐是端到端和从头开始的网络尺度alt-text数据和带注释的图像，通过将所有标签视为文本，无缝地统一自然语言监督以进行表示。从经验上讲，可口可乐通过零拍传输或在广泛的下游任务上进行零摄像转移或最少的特定任务适应，跨越视觉识别（Imagenet，Kinetics-400/600/700，瞬间， ），交叉模式检索（MSCOCO，FLICKR30K，MSR-VTT），多模式理解（VQA，SNLI-VE，NLVR2）和图像字幕（MSCOCO，NOCAPS）。值得注意的是，在Imagenet分类方面，COCA获得了86.3％的TOP-1准确性，带有冷冻编码器和学习的分类头90.6％，以及带有填充编码器的Imagenet上的新最先进的91.0％Top-1 Top-1精度。

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}。

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.

Despite the superior performance brought by vision-and-language pretraining, it remains unclear whether learning with multi-modal data can help understand each individual modality. In this work, we investigate how language can help with visual representation learning from a probing perspective. Specifically, we compare vision-and-language and vision-only models by probing their visual representations on a broad range of tasks, in order to assess the quality of the learned representations in a fine-grained manner. Interestingly, our probing results suggest that vision-and-language models are better at label prediction tasks like object and attribute prediction, while vision-only models are stronger at dense prediction tasks that require more localized information. With further analysis using detailed metrics, our study suggests that language helps vision models learn better semantics, but not localization. Code is released at https://github.com/Lizw14/visual_probing.