什么构成一个物体？这是计算机愿景中的长期问题。为了实现这一目标，已经开发了许多基于学习的基于学习的方法来得分对象。但是，它们通常不会划过新域和未经看不见的对象。在本文中，我们倡导现有方法缺乏由人类可理解的语义管理的自上而下的监督信号。为了弥合这一差距，我们探索了已经用对齐的图像文本对培训的多模态视觉变压器（MVIT）。我们对各个域和新型对象的广泛实验显示了MVITS的最先进的性能，以使图像中的通用对象本地化。基于这些发现，我们使用多尺度特征处理和可变形的自我关注来开发一种高效且灵活的MVIT架构，可以自适应地生成给定特定语言查询的提议。我们展示了MVIT提案在各种应用中的重要性，包括开放世界对象检测，突出和伪装对象检测，监督和自我监督的检测任务。此外，MVITS提供了具有可理解文本查询的增强的交互性。代码：https://git.io/j1hpy。

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

最近对物体检测的自我监督预防方法在很大程度上专注于预先绘制物体探测器的骨干，忽略了检测架构的关键部分。相反，我们介绍了DetReg，这是一种新的自我监督方法，用于预先列出整个对象检测网络，包括对象本地化和嵌入组件。在预先绘制期间，DetReg预测对象本地化以与无监督区域提议生成器匹配本地化，并同时将相应的特征嵌入与自我监控图像编码器的嵌入式对齐。我们使用DETR系列探测器实施DetReg，并显示它在Coco，Pascal VOC和空中客车船基准上的Fineetuned时改善了竞争性基线。在低数据制度中，包括半监督和几秒钟学习设置，DetReg建立了许多最先进的结果，例如，在Coco上，我们看到10次检测和+3.5的AP改进A +6.0 AP改进当培训只有1％的标签时。对于代码和预用模型，请访问https://amirbar.net/detreg的项目页面

现有的开放式视频探测器通常通过利用不同形式的弱监督来扩大其词汇大小。这有助于推断出新的对象。开放式视频检测（OVD）中使用的两种流行形式的弱点，包括预审计的剪辑模型和图像级监督。我们注意到，这两种监督模式均未在检测任务中最佳地对齐：剪辑经过图像文本对培训，并且缺乏对象的精确定位，而图像级监督已与启发式方法一起使用，这些启发式方法无法准确指定本地对象区域。在这项工作中，我们建议通过从剪辑模型中执行以对象为中心的语言嵌入来解决此问题。此外，我们仅使用伪标记的过程来视觉上仅通过图像级监督对象，该过程提供高质量的对象建议，并有助于在训练过程中扩展词汇。我们通过新的重量转移函数在上述两个对象对准策略之间建立桥梁，该策略汇总了它们的免费强度。本质上，提出的模型试图最大程度地减少OVD设置中对象和以图像为中心表示之间的差距。在可可基准上，我们提出的方法在新颖类中实现了40.3 AP50，绝对11.9比以前的最佳性能获得了11.9的增长。对于LVIS，我们超过了5.0 Mask AP的最先进VILD模型，总体上有3.4个。 。代码：https：//bit.ly/3byzoqp。

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.

尽管对象检测方面取得了很大进展，但由于实例级边界盒注释所需的巨大人性化，大多数现有方法都仅限于一小一少量的对象类别。为了减轻问题，最近的开放词汇和零射击检测方法试图检测培训期间未见的对象类别。但是，这些方法仍然依赖于一组基类上手动提供的边界盒注释。我们提出了一个开放的词汇检测框架，可以在没有手动提供边界盒注释的情况下培训。我们的方法通过利用预先训练的视觉语言模型的本地化能力来实现这一目标，并产生可直接用于训练对象探测器的伪边界盒标签。 Coco，Pascal VOC，Objects365和LVIS的实验结果证明了我们方法的有效性。具体而言，我们的方法优于使用人类注释的边界箱训练的最先进（SOTA），即使我们的培训源未配备手动边界盒标签，也可以在COCO新型类别上用3％AP培训。在利用手动边界箱标签作为基线时，我们的方法主要超过8％的AP。

构建强大的通用对象检测框架需要扩展到更大的标签空间和更大的培训数据集。但是，大规模获取数千个类别的注释是高昂的成本。我们提出了一种新颖的方法，该方法利用了最近的视觉和语言模型中可用的丰富语义来将对象定位和分类在未标记的图像中，从而有效地生成了伪标签以进行对象检测。从通用和类别的区域建议机制开始，我们使用视觉和语言模型将图像的每个区域分类为下游任务所需的任何对象类别。我们在两个特定的任务（开放式摄影检测检测）中演示了生成的伪标签的值，其中模型需要概括为看不见的对象类别以及半监督对象检测，其中可以使用其他未标记的图像来改善模型。我们的经验评估显示了伪标签在这两个任务中的有效性，我们在其中优于竞争基准并实现了开放式摄制对象检测的新颖最新。我们的代码可在https://github.com/xiaofeng94/vl-plm上找到。

长期以来，将物体检测推向开放量和几乎没有射击转移一直是计算机视觉研究的挑战。这项工作探讨了一种持续的学习方法，该方法使探测器能够通过多数据远见语言的预训练扩展其零/少量功能。我们使用自然语言作为知识表示，我们探讨了从不同培训数据集积累“视觉词汇”的方法，并将任务统一为语言条件的检测框架。具体而言，我们提出了一种新颖的语言感知探测器OMDET和一种新颖的培训机制。拟议的多模式检测网络可以解决多数据库联合培训中的技术挑战，并且可以推广到任意数量的培训数据集，而无需手动标签分类合并的要求。与单独训练相比，Coco，Pascal VOC和更宽的面部/行人的实验结果通过在关节训练中或更高的分数来证实了疗效。此外，我们对超过400万个独特的对象词汇进行了预先培训，并在ODINW的35个下游任务上评估了所得模型。结果表明，OMDET能够在ODINW上实现最新的微调性能。分析表明，通过扩展提出的预训练方法，OMDET继续改善其零/少量调整性能，这表明了进一步扩展的有希望的方法。

Consider a scenario in one-shot query-guided object localization where neither an image of the object nor the object category name is available as a query. In such a scenario, a hand-drawn sketch of the object could be a choice for a query. However, hand-drawn crude sketches alone, when used as queries, might be ambiguous for object localization, e.g., a sketch of a laptop could be confused for a sofa. On the other hand, a linguistic definition of the category, e.g., a small portable computer small enough to use in your lap" along with the sketch query, gives better visual and semantic cues for object localization. In this work, we present a multimodal query-guided object localization approach under the challenging open-set setting. In particular, we use queries from two modalities, namely, hand-drawn sketch and description of the object (also known as gloss), to perform object localization. Multimodal query-guided object localization is a challenging task, especially when a large domain gap exists between the queries and the natural images, as well as due to the challenge of combining the complementary and minimal information present across the queries. For example, hand-drawn crude sketches contain abstract shape information of an object, while the text descriptions often capture partial semantic information about a given object category. To address the aforementioned challenges, we present a novel cross-modal attention scheme that guides the region proposal network to generate object proposals relevant to the input queries and a novel orthogonal projection-based proposal scoring technique that scores each proposal with respect to the queries, thereby yielding the final localization results. ...

开放世界对象检测（OWOD）是一个具有挑战性的计算机视觉问题，其中任务是检测一组已知的对象类别，同时识别未知对象。此外，该模型必须逐步学习在下一个培训集中所知的新类。不同于标准对象检测，OWOD设置会对在潜在的未知物体上生成质量候选建议的质量挑战，将未知物体与背景中的未知物体分开并检测不同的未知物体。在这里，我们介绍了一种新的基于端到端的变换器的框架OW-DETR，用于开放世界对象检测。建议的OW-DETR包括三个专用组成部分，即注意力驱动的伪标签，新颖性分类和对象评分，以明确地解决上述OWOD挑战。我们的OW-DETR明确地编码了多尺度上下文信息，具有较少的归纳偏差，使得从已知类传输到未知类，并且可以更好地区分未知对象和背景之间。综合实验是对两个基准进行的：MS-Coco和Pascal VOC。广泛的消融揭示了我们拟议的贡献的优点。此外，我们的模型优于最近引入的OWOD方法矿石，绝对增益在MS-Coco基准测试中的未知召回方面的1.8％至3.3％。在增量对象检测的情况下，OW-DETR以Pascal VOC基准上的所有设置优于最先进的。我们的代码和模型将公开发布。

Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ideally, we would like to extend an open-vocabulary detector such that it can produce bounding box predictions based on user inputs in form of either natural language or exemplar image. This offers great flexibility and user experience for human-computer interaction. To this end, we propose a novel open-vocabulary detector based on DETR -- hence the name OV-DETR -- which, once trained, can detect any object given its class name or an exemplar image. The biggest challenge of turning DETR into an open-vocabulary detector is that it is impossible to calculate the classification cost matrix of novel classes without access to their labeled images. To overcome this challenge, we formulate the learning objective as a binary matching one between input queries (class name or exemplar image) and the corresponding objects, which learns useful correspondence to generalize to unseen queries during testing. For training, we choose to condition the Transformer decoder on the input embeddings obtained from a pre-trained vision-language model like CLIP, in order to enable matching for both text and image queries. With extensive experiments on LVIS and COCO datasets, we demonstrate that our OV-DETR -- the first end-to-end Transformer-based open-vocabulary detector -- achieves non-trivial improvements over current state of the arts.

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。

开放世界对象检测是一个更具笼统和挑战性的目标，旨在识别和本地化由任意类别名称描述的对象。最近的工作GLIP通过将检测数据集的所有类别名称连接到句子中，从而将此问题作为接地问题，从而导致类别名称之间的效率低下的相互作用。本文介绍了Distclip，这是一种通过诉诸于设计概念词典的知识富集，是一种平行的视觉概念训练预训练方法，用于开放世界检测。为了提高学习效率，我们提出了一种新型的并行概念公式，该公式分别提取概念，以更好地利用异质数据集（即检测，接地和图像文本对）进行培训。我们进一步设计了来自各种在线资源和检测数据集的概念字典〜（带有描述），以提供每个概念的先验知识。通过用描述丰富这些概念，我们明确地建立了各种概念之间的关系，以促进开放域学习。所提出的概念词典进一步用于提供足够的负面概念，用于构建单词区域对齐损失\，并完成图像对文本对数据标题中缺少描述的对象的标签。所提出的框架显示出强烈的零射击性能性能，例如，在LVIS数据集上，我们的DETCLIP-T优于9.9％的地图GLIPT-T优于GLIP-T，并且与完全避免的型号相比，稀有类别的稀有类别提高了13.5％。作为我们的。

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。

在这项工作中，我们提出了一种开放式摄制对象检测方法，该方法基于图像映射对，学会了检测新颖对象类别以及给定的一组已知类别。这是一种两阶段的训练方法，首先使用位置引导的图像捕获匹配技术以弱监督的方式学习新颖和已知类别的类标签，第二个使用已知的类注释专用于对象检测任务的模型。我们表明，一个简单的语言模型比检测新对象的大型上下文化语言模型更适合。此外，我们引入了一种一致性调查技术，以更好地利用图像捕获对信息。我们的方法比较与现有的开放式检测方法相比，同时具有数据效率。源代码可从https://github.com/lmb-freiburg/locov获得。

Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet.

现有语言接地模型通常使用对象提案瓶颈：预先训练的探测器提出了场景中的对象，模型学会从这些框提案中选择答案，而不会参加原始图像或3D点云。对象探测器通常在固定词汇上培训，其对象和属性通常过于限制开放域语言接地，其中话语可以指在各种抽象层面的视觉实体，例如椅子，椅子的椅子，或椅子前腿的尖端。我们为3D场景提出了一个用于接地语言的模型，绕过具有三个主要创新的盒子提案瓶颈：i）横跨语言流的迭代注意，点云特征流和3D框提案。 ii）具有非参数实体查询的变压器解码器，用于对对象和部分参考进行解码3D框。 iii）通过将物体检测视为由候选类别标签列表的参考词汇的接地为基础，从3D对象注释和语言接地注释的联合监督。这些创新在流行的3D语言接地基准上之前的方法上产生了显着的定量收益（对SR3D基准测试的绝对改善）。我们消除了我们的每一个创新，向模型表现出贡献。当在具有次要变化的2D图像上应用于语言接地时，它会与最先进的，同时收敛于GPU时间的一半。代码和检查点将在https://github.com/nickgkan/beaut_detr中提供

Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery. The proposed approach is a two-stage framework. First, instances of object parts are segmented by using the intra-image similarity between self-supervised local features. The second step merges and filters the object parts to form complete object instances. The latter is performed by two CNN models that capture semantic information on objects from the entire dataset. We demonstrate that the pseudo-labels generated by our method provide a better precision-recall trade-off than existing single and multiple objects discovery methods. In particular, we provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision. Furthermore, the exact classes of the unknown objects must be identified without catastrophic forgetting of the previous known classes when the corresponding annotations of unknown objects are given incrementally. In this paper, we propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. In the first stage, we pre-train a model on the current annotated data to detect objects from the current known classes, and concurrently train an additional binary classifier to classify predictions into foreground or background classes. This helps the model to build an unbiased feature representations that can facilitate the detection of unknown classes in subsequent process. In the second stage, we fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Furthermore, we alleviate catastrophic forgetting when the annotations of the unknown classes becomes available incrementally by using knowledge distillation and exemplar replay. Experimental results on PASCAL VOC and MS-COCO show that our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.