在计算机视觉中，微调是利用预训练的视觉模型来执行下游任务的事实上的方法。但是，由于采用参数效率低下的全局更新并严重依赖于高质量的下游数据，因此在实践中部署它是非常具有挑战性的。最近，基于及时的学习添加了与任务相关的提示，以使下游任务适应预训练的模型，从而极大地提高了许多自然语言下游任务的性能。在这项工作中，我们扩展了这种显着的转移能力，从迅速的愿景模型中受益，以替代微调。为此，我们提出了参数有效的及时调整（亲调整），以使冷冻视觉模型适应各种下游视觉任务。实行调整的关键是基于及时的调整，即学习特定于任务的视觉提示，以使用预先训练的模型冷冻的下游输入图像。通过仅培训一些其他参数，它可以在基于CNN和基于变压器的各种架构上工作。广泛的实验证据表明，在广泛的视觉任务和场景中，主张表现优于微调，包括图像分类（通用对象，类失衡，图像腐败，对抗性稳定性和分布范围内的概括）和密集的预测任务例如对象检测和语义分割。

这项工作的目的是探索如何有效有效地将预训练的基础模型适应图像语义分割的各种下游任务。常规方法通常为每个特定数据集微调整个网络，并且存储这些网络的大量参数是繁重的。最近的一些作品试图将一些可训练的参数插入冷冻网络中，以学习有效调整的视觉提示。但是，这些作品显着修改了标准模块的原始结构，使其在许多现有的高速推理设备上无法使用，其中标准模块及其参数已嵌入。为了促进基于及时的语义细分，我们提出了一个新颖的阶段间及时匹配的框架，该框架保持基础模型的原始结构，同时自适应地生成视觉提示，以适应以任务为导向的调整。具体而言，首先将预训练的模型分为多个阶段，其参数被冷冻并共享所有语义分割任务。然后将称为语义意识的提示匹配器的轻巧模块在两个阶段之间介绍给层次上的插值，以在临时语义图的指导下学习每个特定任务的合理提示。这样，我们可以更好地刺激对冷冻模型的预训练的知识，以有效地学习下游数据集的语义概念。在五个基准上进行的广泛实验表明，所提出的方法可以实现参数效率和性能效率之间的有希望的权衡。

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance of full fine-tuning. In this way, SSF also surprisingly outperforms other parameter-efficient fine-tuning approaches even with a smaller number of tunable parameters. Furthermore, different from some existing parameter-efficient fine-tuning methods (e.g., Adapter or VPT) that introduce the extra parameters and computational cost in the training and inference stages, SSF only adds learnable parameters during the training stage, and these additional parameters can be merged into the original pre-trained model weights via re-parameterization in the inference phase. With the proposed SSF, our model obtains 2.46% (90.72% vs. 88.54%) and 11.48% (73.10% vs. 65.57%) performance improvement on FGVC and VTAB-1k in terms of Top-1 accuracy compared to the full fine-tuning but only fine-tuning about 0.3M parameters. We also conduct amounts of experiments in various model families (CNNs, Transformers, and MLPs) and datasets. Results on 26 image classification datasets in total and 3 robustness & out-of-distribution datasets show the effectiveness of SSF. Code is available at https://github.com/dongzelian/SSF.

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

从自然语言监督中学习视觉表示，最近在许多开创性的作品中表现出了巨大的希望。通常，这些具有语言的视觉模型表现出对各种数据集和任务的强大可传递性。但是，由于缺乏易于使用的评估工具包和公共基准，评估这些模型的可转让性仍然很具有挑战性。为了解决这个问题，我们构建了高级版（评估语言的视觉任务级传输），这是用于评估（预训练）语言增强视觉模型的第一个基准和工具包。升华由三个组成部分组成。 （i）数据集。作为下游评估套件，它由20个图像分类数据集和35个对象检测数据集组成，每个数据集都用外部知识来增强。 （ii）工具包。开发了自动高参数调谐工具包，以促进下游任务的模型评估。 （iii）指标。多种评估指标用于测量样品效率（零射击和少量）和参数效率（线性探测和完整模型微调）。我们在https://computer-vision-in-the-wild.github.io/elevater/上公开发布leverater

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.

尽管参数有效调整（PET）方法在自然语言处理（NLP）任务上显示出巨大的潜力，但其有效性仍然对计算机视觉（CV）任务的大规模转向进行了研究。本文提出了Conv-Adapter，这是一种专为CONCNET设计的PET模块。 Conv-Adapter具有轻巧的，可转让的域和架构，不合时宜，并且在不同的任务上具有广义性能。当转移下游任务时，Conv-Adapter将特定于任务的特征调制到主链的中间表示，同时保持预先训练的参数冻结。通过仅引入少量可学习的参数，例如，仅3.5％的RESNET50的完整微调参数，Conv-Adapter优于先前的宠物基线方法，并实现可比性或超过23个分类任务的全面调查的性能。它还在几乎没有分类的情况下表现出卓越的性能，平均利润率为3.39％。除分类外，Conv-Adapter可以推广到检测和细分任务，其参数降低了50％以上，但性能与传统的完整微调相当。

当前的Modus Operandi在改编预训练的模型中涉及更新所有骨干参数，即，完整的微调。本文介绍了视觉及时调整（VPT），作为视觉中大规模变压器模型的全面微调的有效替代方案。VPT从最近有效地调整大型语言模型的最新进展中汲取灵感，在输入空间中仅引入了少量的可训练参数（少于模型参数），同时保持模型骨架冻结。通过对各种下游识别任务的广泛实验，我们表明VPT与其他参数有效调整协议相比获得了显着的性能增长。最重要的是，在许多情况下，VPT甚至在模型能力和培训数据量表的许多情况下都胜过全面的微调，同时降低了每任务的存储成本。

We propose a new neural network design paradigm Reversible Column Network (RevCol). The main body of RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does. Our experiments suggest that CNN-style RevCol models can achieve very competitive performances on multiple computer vision tasks such as image classification, object detection and semantic segmentation, especially with large parameter budget and large dataset. For example, after ImageNet-22K pre-training, RevCol-XL obtains 88.2% ImageNet-1K accuracy. Given more pre-training data, our largest model RevCol-H reaches 90.0% on ImageNet-1K, 63.8% APbox on COCO detection minival set, 61.0% mIoU on ADE20k segmentation. To our knowledge, it is the best COCO detection and ADE20k segmentation result among pure (static) CNN models. Moreover, as a general macro architecture fashion, RevCol can also be introduced into transformers or other neural networks, which is demonstrated to improve the performances in both computer vision and NLP tasks. We release code and models at https://github.com/megvii-research/RevCol

Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language models to multiple downstream tasks. However, existing approaches usually consider learning prompt vectors for each task independently from scratch, thereby failing to exploit the rich shareable knowledge across different vision-language tasks. In this paper, we propose multitask vision-language prompt tuning (MVLPT), which incorporates cross-task knowledge into prompt tuning for vision-language models. Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning. We benchmark the proposed MVLPT using three representative prompt tuning methods, namely text prompt tuning, visual prompt tuning, and the unified vision-language prompt tuning. Results in 20 vision tasks demonstrate that the proposed approach outperforms all single-task baseline prompt tuning methods, setting the new state-of-the-art on the few-shot ELEVATER benchmarks and cross-task generalization benchmarks. To understand where the cross-task knowledge is most effective, we also conduct a large-scale study on task transferability with 20 vision tasks in 400 combinations for each prompt tuning method. It shows that the most performant MVLPT for each prompt tuning method prefers different task combinations and many tasks can benefit each other, depending on their visual similarity and label similarity. Code is available at https://github.com/sIncerass/MVLPT.

为了同时朝着对多个下游任务的整体理解，需要提取具有更好可传递性的功能。尽管许多最新的自我监管的预训练方法在普遍的预处理前范式下在各种视觉任务上取得了令人印象深刻的表现，但它们对多任务学习方案的概括能力尚待探索。在本文中，我们在三个下游任务上进行了广泛研究各种类型的自我监督方法的转移性能，例如Moco和Simc​​lr，包括语义细分，可驱动的区域细分和交通对象检测，在大规模驾驶数据集中BDD100K。我们出人意料地发现，他们的表现是最佳的甚至落后于单任务基线的滞后，这可能是由于训练目标和建筑设计的区别在于预处理范式。为了克服这一难题，并避免重新设计资源密集的预培训阶段，我们提出了一种简单而有效的预处理 - 适应性 - 赛范围，用于一般的多任务培训，可以有效地适应现行预审预周态的模型没有增加培训开销。在自适应阶段，我们利用可学习的多尺度适配器来动态调整由多任务目标监督的预验证的模型权重，同时使经过预告片的知识未经触及。此外，我们将视觉语言预训练模型剪辑视为对预处理 - 适应 - 最终范式的强烈补充，并提出了一个名为LV-Adapter的新型适配器，该适配器通过任务特定的提示将语言先验纳入了多任务的模型中和视觉和文本特征之间的对齐。

最近出现了有希望的表现，利用大型预训练的模型来实现各种感兴趣的下游任务。由于模型的规模不断增长，因此，在模型培训和存储方面，基于标准的完整任务适应策略的成本高昂。这导致了参数有效传输学习的新研究方向。但是，现有的尝试通常集中在预训练模型的相同模式（例如图像理解）的下游任务上。这会产生限制，因为在某些特定的方式（例如，视频理解）中，具有足够知识的强大预训练模型较少或不可用。在这项工作中，我们研究了这样一种新型的跨模式转移学习设置，即参数有效的图像到视频传输学习。为了解决此问题，我们为每个视频任务提出了一个新的时空适配器（ST-ADAPTER），以进行参数有效调整。凭借紧凑设计中的内置时空推理能力，ST-ADAPTER可以实现预训练的图像模型，而无需时间知识，以小（〜8％）的每任务参数成本来理解动态视频内容，以大约需要与以前的工作相比，更新参数少20倍。在视频动作识别任务上进行的广泛实验表明，我们的ST-ADAPTER可以匹配甚至优于强大的完整微调策略和最先进的视频模型，同时享受参数效率的优势。

视觉识别的“咆哮20S”开始引入视觉变压器（VITS），这将被取代的Cummnets作为最先进的图像分类模型。另一方面，vanilla vit，当应用于一般计算机视觉任务等对象检测和语义分割时面临困难。它是重新引入多个ConvNet Priors的等级变压器（例如，Swin变压器），使变压器实际上可作为通用视觉骨干网，并在各种视觉任务上展示了显着性能。然而，这种混合方法的有效性仍然在很大程度上归功于变压器的内在优越性，而不是卷积的固有感应偏差。在这项工作中，我们重新审视设计空间并测试纯粹的Convnet可以实现的限制。我们逐渐“现代化”标准Reset朝着视觉变压器的设计设计，并发现几个有助于沿途绩效差异的关键组件。此探索的结果是一个纯粹的ConvNet型号被称为ConvNext。完全由标准的Convnet模块构建，ConvNexts在准确性和可扩展性方面与变压器竞争，实现了87.8％的ImageNet Top-1精度和表现优于COCO检测和ADE20K分割的Swin变压器，同时保持了标准Convnet的简单性和效率。

如今，大规模数据集的大型培训大型模型已成为深度学习的关键主题。具有较高表示能力和可传递性的预训练模型取得了巨大的成功，并在自然语言处理和2D视觉中占据了许多下游任务。但是，鉴于有限的训练数据相对不便，因此将这种预处理的调整范式促进这种预处理的调整范式是非平凡的。在本文中，我们提供了一个新的观点，即利用3D域中的预训练的2D知识来解决此问题，以新颖的点对像素来调整预训练的图像模型，以较小的参数成本提示点云分析。遵循促使工程的原理，我们将点云转换为具有几何形状的投影和几何学吸引着色的色彩图像，以适应预训练的图像模型，在点云分析的端到端优化期间，其权重冻结了任务。我们进行了广泛的实验，以证明与提议的点对像素提示合作，更好的预训练图像模型将导致在3D视觉中始终如一地表现更好的性能。享受图像预训练领域的繁荣发展，我们的方法在Scanobjectnn的最困难环境中获得了89.3％的精度，超过了传统的点云模型，具有较少的可训练参数。我们的框架在模型网分类和塑形部分分割方面还表现出非常具竞争力的性能。代码可从https://github.com/wangzy22/p2p获得

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.

视觉变压器的最新进展在基于点产生自我注意的新空间建模机制驱动的各种任务中取得了巨大成功。在本文中，我们表明，视觉变压器背后的关键要素，即输入自适应，远程和高阶空间相互作用，也可以通过基于卷积的框架有效地实现。我们介绍了递归封闭式卷积（$ \ textit {g}^\ textit {n} $ conv），该卷积{n} $ conv）与封闭的卷积和递归设计执行高阶空间交互。新操作是高度灵活和可定制的，它与卷积的各种变体兼容，并将自我注意的两阶相互作用扩展到任意订单，而无需引入大量额外的计算。 $ \ textit {g}^\ textit {n} $ conv可以用作插件模块，以改善各种视觉变压器和基于卷积的模型。根据该操作，我们构建了一个名为Hornet的新型通用视觉骨干家族。关于ImageNet分类，可可对象检测和ADE20K语义分割的广泛实验表明，大黄蜂的表现优于Swin变形金刚，并具有相似的整体体系结构和训练配置的明显边距。大黄蜂还显示出对更多训练数据和更大模型大小的有利可伸缩性。除了在视觉编码器中的有效性外，我们还可以将$ \ textit {g}^\ textit {n} $ conv应用于特定于任务的解码器，并始终通过较少的计算来提高密集的预测性能。我们的结果表明，$ \ textIt {g}^\ textit {n} $ conv可以成为视觉建模的新基本模块，可有效结合视觉变形金刚和CNN的优点。代码可从https://github.com/raoyongming/hornet获得

场景图生成（SGG）是一项基本任务，旨在检测图像中对象之间的视觉关系。流行的SGG方法要求在培训集中给出所有对象类。这样的封闭设置限制了SGG的实际应用。在本文中，我们介绍了开放式视频范围场景图生成，这是一种新颖，现实且具有挑战性的环境，其中模型在一组基本对象类上进行了训练，但需要推断出看不见的目标对象类的关系。为此，我们提出了一种两步方法，该方法首先对大量的粗粒区域捕获数据进行预先培训，然后利用两种基于及时的技术来验证预先训练的模型而无需更新其参数。此外，我们的方法可以支持对完全看不见的对象类的推论，而现有方法无法处理。在三个基准数据集（视觉基因组，GQA和开放图像）上进行的广泛实验，我们的方法在OV-SGG的设置以及常规的封闭SGG上明显优于最近的强大SGG方法。

视觉世界自然地展现了一个长尾的开放类分布，这对现代视觉系统带来了巨大挑战。现有方法可以执行类重新平衡策略或直接改进网络模块以解决问题。然而，他们仍然用有限一套预定义标签训练模型，限制了他们的监督信息并限制了他们对新颖实例的可转移性。新途径上的大型对比视觉普瑞宁普雷宁闪光灯的最新进展，可视识别。利用开放词汇监督，预先染色的对比视觉语言模型学习强大的多模式表示，这是对处理数据缺陷和看不见的概念。通过计算视觉和文本输入之间的语义相似性，可视识别被转换为vision语言匹配问题。灵感来自于此，我们提出了民谣，利用了对比尾识别的对比视觉模型。我们首先通过对特定的长尾目标数据集进行对比学习继续预先预留视觉语言骨干。之后，我们冻结了骨干，进一步采用了额外的适配器层，以增强通过重新采样策略构建的平衡训练样本上的尾级课程的表示。已经在三个流行的长尾识别基准测试中进行了广泛的实验。因此，我们简单有效的方法设定了新的最先进的表演，优于具有大边距的竞争基础。代码在https://github.com/gaopengcuhk/ballad发布。

With the ever-growing model size and the limited availability of labeled training data, transfer learning has become an increasingly popular approach in many science and engineering domains. For classification problems, this work delves into the mystery of transfer learning through an intriguing phenomenon termed neural collapse (NC), where the last-layer features and classifiers of learned deep networks satisfy: (i) the within-class variability of the features collapses to zero, and (ii) the between-class feature means are maximally and equally separated. Through the lens of NC, our findings for transfer learning are the following: (i) when pre-training models, preventing intra-class variability collapse (to a certain extent) better preserves the intrinsic structures of the input data, so that it leads to better model transferability; (ii) when fine-tuning models on downstream tasks, obtaining features with more NC on downstream data results in better test accuracy on the given task. The above results not only demystify many widely used heuristics in model pre-training (e.g., data augmentation, projection head, self-supervised learning), but also leads to more efficient and principled fine-tuning method on downstream tasks that we demonstrate through extensive experimental results.

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.