最近自我监督学习成功的核心组成部分是裁剪数据增强，其选择要在自我监督损失中用作正视图的图像的子区域。底层假设是给定图像的随机裁剪和调整大小的区域与感兴趣对象的信息共享信息，其中学习的表示将捕获。这种假设在诸如想象网的数据集中大多满足，其中存在大，以中心为中心的对象，这很可能存在于完整图像的随机作物中。然而，在诸如OpenImages或Coco的其他数据集中，其更像是真实世界未保健数据的代表，通常存在图像中的多个小对象。在这项工作中，我们表明，基于通常随机裁剪的自我监督学习在此类数据集中表现不佳。我们提出用从对象提案算法获得的作物取代一种或两种随机作物。这鼓励模型学习对象和场景级别语义表示。使用这种方法，我们调用对象感知裁剪，导致对分类和对象检测基准的场景裁剪的显着改进。例如，在OpenImages上，我们的方法可以使用基于Moco-V2的预训练来实现8.8％的提高8.8％地图。我们还显示了对Coco和Pascal-Voc对象检测和分割任务的显着改善，通过最先进的自我监督的学习方法。我们的方法是高效，简单且通用的，可用于最现有的对比和非对比的自我监督的学习框架。

Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations for objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in a hyperbolic space. This novel hyperbolic objective encourages the scene-object hypernymy among the representations by optimizing the magnitude of their norms. We show that when pretraining on the COCO and OpenImages datasets, the hyperbolic loss improves downstream performance of several baselines across multiple datasets and tasks, including image classification, object detection, and semantic segmentation. We also show that the properties of the learned representations allow us to solve various vision tasks that involve the interaction between scenes and objects in a zero-shot fashion. Our code can be found at \url{https://github.com/shlokk/HCL/tree/main/HCL}.

在这项工作中，我们研究了对象检测模型的自我监督预审计的不同方法。我们首先设计一个通用框架，通过随机采样和投射框来学习从图像中学习空间一致的密集表示，并将其投影到每个增强视图，并最大程度地提高相应的盒子功能之间的相似性。我们研究文献中的现有设计选择，例如盒子生成，功能提取策略，并使用其在实例级图像表示学习技术上获得成功启发的多种视图。我们的结果表明，该方法对超参数的不同选择是可靠的，并且使用多个视图不如实例级图像表示学习所显示的那样有效。我们还设计了两个辅助任务，以通过（1）通过使用对比度损失从采样设置中预测盒子中的一个视图中的框来预测框，并且（2）使用变压器预测盒子坐标，这可能会受益。下游对象检测任务。我们发现，在标记数据上预审计的模型时，这些任务不会导致更好的对象检测性能。

Contrastive learning has emerged as a competitive pretraining method for object detection. Despite this progress, there has been minimal investigation into the robustness of contrastively pretrained detectors when faced with domain shifts. To address this gap, we conduct an empirical study of contrastive learning and out-of-domain object detection, studying how contrastive view design affects robustness. In particular, we perform a case study of the detection-focused pretext task Instance Localization (InsLoc) and propose strategies to augment views and enhance robustness in appearance-shifted and context-shifted scenarios. Amongst these strategies, we propose changes to cropping such as altering the percentage used, adding IoU constraints, and integrating saliency based object priors. We also explore the addition of shortcut-reducing augmentations such as Poisson blending, texture flattening, and elastic deformation. We benchmark these strategies on abstract, weather, and context domain shifts and illustrate robust ways to combine them, in both pretraining on single-object and multi-object image datasets. Overall, our results and insights show how to ensure robustness through the choice of views in contrastive learning.

自我监督学习的最新进展证明了多种视觉任务的有希望的结果。高性能自我监督方法中的一个重要成分是通过培训模型使用数据增强，以便在嵌入空间附近的相同图像的不同增强视图。然而，常用的增强管道整体地对待图像，忽略图像的部分的语义相关性-e.g。主题与背景 - 这可能导致学习杂散相关性。我们的工作通过调查一类简单但高度有效的“背景增强”来解决这个问题，这鼓励模型专注于语义相关内容，劝阻它们专注于图像背景。通过系统的调查，我们表明背景增强导致在各种任务中跨越一系列最先进的自我监督方法（MOCO-V2，BYOL，SWAV）的性能大量改进。 $ \ SIM $ + 1-2％的ImageNet收益，使得与监督基准的表现有关。此外，我们发现有限标签设置的改进甚至更大（高达4.2％）。背景技术增强还改善了许多分布换档的鲁棒性，包括天然对抗性实例，想象群-9，对抗性攻击，想象成型。我们还在产生了用于背景增强的显着掩模的过程中完全无监督的显着性检测进展。

我们提出了一种适用于半全球任务的自学学习（SSL）方法，例如对象检测和语义分割。我们通过在训练过程中最大程度地减少像素级局部对比度（LC）损失，代表了同一图像转换版本的相应图像位置之间的局部一致性。可以将LC-LOSS添加到以最小开销的现有自我监督学习方法中。我们使用可可，Pascal VOC和CityScapes数据集评估了两个下游任务的SSL方法 - 对象检测和语义细分。我们的方法的表现优于现有的最新SSL方法可可对象检测的方法1.9％，Pascal VOC检测1.4％，而CityScapes Sementation则为0.6％。

对比的自我监督学习在很大程度上缩小了对想象成的预先训练的差距。然而，它的成功高度依赖于想象成的以对象形象，即相同图像的不同增强视图对应于相同的对象。当预先训练在具有许多物体的更复杂的场景图像上，如此重种策划约束会立即不可行。为了克服这一限制，我们介绍了对象级表示学习（ORL），这是一个新的自我监督的学习框架迈向场景图像。我们的主要洞察力是利用图像级自我监督的预培训作为发现对象级语义对应之前的，从而实现了从场景图像中学习的对象级表示。对Coco的广泛实验表明，ORL显着提高了自我监督学习在场景图像上的性能，甚至超过了在几个下游任务上的监督Imagenet预训练。此外，当可用更加解标的场景图像时，ORL提高了下游性能，证明其在野外利用未标记数据的巨大潜力。我们希望我们的方法可以激励未来的研究从场景数据的更多通用无人监督的代表。

To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning (DenseCL), which implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images.Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin. Specifically, over the strong MoCo-v2 baseline, our method achieves significant improvements of 2.0% AP on PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8% mIoU on Cityscapes semantic segmentation.

自我监督学习（SSL）的承诺是利用大量未标记的数据来解决复杂的任务。尽管简单，图像级学习取得了出色的进步，但最新方法显示出包括图像结构知识的优势。但是，通过引入手工制作的图像分割来定义感兴趣的区域或专门的增强策略，这些方法牺牲了使SSL如此强大的简单性和通用性。取而代之的是，我们提出了一个自我监督的学习范式，该学习范式本身会发现这种图像结构。我们的方法，ODIN，夫妻对象发现和表示网络，以发现有意义的图像分割，而无需任何监督。由此产生的学习范式更简单，更易碎，更一般，并且取得了最先进的转移学习结果，以进行对象检测和实例对可可的细分，以及对Pascal和CityScapes的语义细分，同时超过监督的预先培训，用于戴维斯的视频细分。

We present DetCo, a simple yet effective self-supervised approach for object detection. Unsupervised pre-training methods have been recently designed for object detection, but they are usually deficient in image classification, or the opposite. Unlike them, DetCo transfers well on downstream instance-level dense prediction tasks, while maintaining competitive image-level classification accuracy. The advantages are derived from (1) multi-level supervision to intermediate representations, (2) contrastive learning between global image and local patches. These two designs facilitate discriminative and consistent global and local representation at each level of feature pyramid, improving detection and classification, simultaneously.Extensive experiments on VOC, COCO, Cityscapes, and ImageNet demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification. For example, on ImageNet classification, DetCo is 6.9% and 5.0% top-1 accuracy better than InsLoc and DenseCL, which are two contemporary works designed for object detection. Moreover, on COCO detection, DetCo is 6.9 AP better than SwAV with Mask R-CNN C4. Notably, DetCo largely boosts up Sparse R-CNN, a recent strong detector, from 45.0 AP to 46.5 AP (+1.5 AP), establishing a new SOTA on COCO. Code is available.

Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction. We compare their performance to a supervised baseline and show that on most tasks the best self-supervised models outperform supervision, confirming the recently observed trend in the literature. We find ImageNet Top-1 accuracy to be highly correlated with transfer to many-shot recognition, but increasingly less so for few-shot, object detection and dense prediction. No single self-supervised method dominates overall, suggesting that universal pre-training is still unsolved. Our analysis of features suggests that top self-supervised learners fail to preserve colour information as well as supervised alternatives, but tend to induce better classifier calibration, and less attentive overfitting than supervised learners.

基于对比的学习的预培训的目标是利用大量的未标记数据来产生可以容易地调整下游的模型。电流方法围绕求解图像辨别任务：给定锚图像，该图像的增强对应物和一些其他图像，该模型必须产生表示，使得锚和其对应物之间的距离很小，并且锚和其他图像很大。这种方法存在两个重要问题：（i）通过对比图像级别的表示，很难生成有利于下游对象级任务（如实例分段）的详细对象敏感功能; （ii）制造增强对应的增强策略是固定的，在预培训的后期阶段做出更低的学习。在这项工作中，我们引入课程对比对象级预培训（CCOP）来解决这些问题：（i）我们使用选择性搜索来查找粗略对象区域并使用它们构建图像间对象级对比度损耗和一个图像内对象级别歧视损失进入我们的预训练目标; （ii）我们提出了一种课程学习机制，其自适应地增强所生成的区域，这允许模型一致地获取有用的学习信号，即使在预训练的后期阶段也是如此。我们的实验表明，当在多对象场景图像数据集上进行预训练时，我们的方法通过大量对象级任务的大幅度提高了MoCo V2基线。代码可在https://github.com/chenhongyiyang/ccop中找到。

本文介绍了密集的暹罗网络（Denseiam），这是一个简单的无监督学习框架，用于密集的预测任务。它通过以两种类型的一致性（即像素一致性和区域一致性）之间最大化一个图像的两个视图之间的相似性来学习视觉表示。具体地，根据重叠区域中的确切位置对应关系，Denseiam首先最大化像素级的空间一致性。它还提取一批与重叠区域中某些子区域相对应的区域嵌入，以形成区域一致性。与以前需要负像素对，动量编码器或启发式面膜的方法相反，Denseiam受益于简单的暹罗网络，并优化了不同粒度的一致性。它还证明了简单的位置对应关系和相互作用的区域嵌入足以学习相似性。我们将Denseiam应用于ImageNet，并在各种下游任务上获得竞争性改进。我们还表明，只有在一些特定于任务的损失中，简单的框架才能直接执行密集的预测任务。在现有的无监督语义细分基准中，它以2.1 miou的速度超过了最新的细分方法，培训成本为28％。代码和型号在https://github.com/zwwwayne/densesiam上发布。

标记数据通常昂贵且耗时，特别是对于诸如对象检测和实例分割之类的任务，这需要对图像的密集标签进行密集的标签。虽然几张拍摄对象检测是关于培训小说中的模型（看不见的）对象类具有很少的数据，但它仍然需要在许多标记的基础（见）类的课程上进行训练。另一方面，自我监督的方法旨在从未标记数据学习的学习表示，该数据转移到诸如物体检测的下游任务。结合几次射击和自我监督的物体检测是一个有前途的研究方向。在本调查中，我们审查并表征了几次射击和自我监督对象检测的最新方法。然后，我们给我们的主要外卖，并讨论未来的研究方向。https://gabrielhuang.github.io/fsod-survey/的项目页面

Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions. In this paper, we introduce pixel-level pretext tasks for learning dense feature representations. The first task directly applies contrastive learning at the pixel level. We additionally propose a pixel-to-propagation consistency task that produces better results, even surpassing the state-of-the-art approaches by a large margin. Specifically, it achieves 60.2 AP, 41.4 / 40.5 mAP and 77.2 mIoU when transferred to Pascal VOC object detection (C4), COCO object detection (FPN / C4) and Cityscapes semantic segmentation using a ResNet-50 backbone network, which are 2.6 AP, 0.8 / 1.0 mAP and 1.0 mIoU better than the previous best methods built on instance-level contrastive learning. Moreover, the pixel-level pretext tasks are found to be effective for pretraining not only regular backbone networks but also head networks used for dense downstream tasks, and are complementary to instance-level contrastive methods. These results demonstrate the strong potential of defining pretext tasks at the pixel level, and suggest a new path forward in unsupervised visual representation learning. Code is available at https://github.com/zdaxie/PixPro.

对比度学习的许多最新方法已努力弥补在ImageNet等标志性图像和Coco等复杂场景上进行预处理的预处理之间的差距。这一差距之所以存在很大程度上是因为普遍使用的随机作物增强量在不同物体的拥挤场景图像中获得语义上不一致的内容。以前的作品使用预处理管道来定位明显的对象以改进裁剪，但是端到端的解决方案仍然难以捉摸。在这项工作中，我们提出了一个框架，该框架通过共同学习表示和细分来实现这一目标。我们利用分割掩码来训练具有掩模依赖性对比损失的模型，并使用经过部分训练的模型来引导更好的掩模。通过在这两个组件之间进行迭代，我们将分割信息中的对比度更新进行基础，并同时改善整个训练的分割。实验表明我们的表示形式在分类，检测和分割方面鲁棒性转移到下游任务。

The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10× or 100×? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between 'enormous data' and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pretraining) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-theart results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or "views") of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a "swapped" prediction mechanism where we predict the code of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.

事实证明，无监督的表示学习方法在学习目标数据集的视觉语义方面有效。这些方法背后的主要思想是，同一图像的不同视图代表相同的语义。在本文中，我们进一步引入了一个附加模块，以促进对样品之间空间跨相关性的知识注入。反过来，这导致了类内部信息的提炼，包括特征级别的位置和同类实例之间的相似性。建议的附加组件可以添加到现有方法中，例如SWAV。稍后，我们可以删除用于推理的附加模块，而无需修改学识的权重。通过一系列广泛的经验评估，我们验证我们的方法在检测类激活图，TOP-1分类准确性和下游任务（例如对象检测）的情况下会提高性能，并具有不同的配置设置。

我们专注于更好地理解增强不变代表性学习的关键因素。我们重新访问moco v2和byol，并试图证明以下假设的真实性：不同的框架即使具有相同的借口任务也会带来不同特征的表示。我们建立了MoCo V2和BYOL之间公平比较的第一个基准，并观察：（i）复杂的模型配置使得可以更好地适应预训练数据集； （ii）从实现竞争性转移表演中获得的预训练和微调阻碍模型的优化策略不匹配。鉴于公平的基准，我们进行进一步的研究并发现网络结构的不对称性赋予对比框架在线性评估协议下正常工作，同时可能会损害长尾分类任务的转移性能。此外，负样本并不能使模型更明智地选择数据增强，也不会使不对称网络结构结构。我们相信我们的发现为将来的工作提供了有用的信息。