多标签图像分类是计算机愿景中的基本但具有挑战性的任务。在过去的几十年里，解决方案探索语义标签之间的关系取得了很大进展。然而，标签的潜在空间上下文信息被剥削。为了解决这个问题，提出了一种空间背景感知的深神经网络，以考虑语义和空间信息的考虑标签。在Microsoft Coco和Pascal VOC上评估了这一提议的框架，用于图像多标签的两个广泛使用的基准数据集。结果表明，该方法优于处理多标签图像分类问题的最先进解决方案。

零拍摄对象检测（ZSD），将传统检测模型扩展到检测来自Unseen类别的对象的任务，已成为计算机视觉中的新挑战。大多数现有方法通过严格的映射传输策略来解决ZSD任务，这可能导致次优ZSD结果：1）这些模型的学习过程忽略了可用的看不见的类信息，因此可以轻松地偏向所看到的类别; 2）原始视觉特征空间并不合适，缺乏歧视信息。为解决这些问题，我们开发了一种用于ZSD的新型语义引导的对比网络，命名为Contrastzsd，一种检测框架首先将对比学习机制带入零拍摄检测的领域。特别地，对比度包括两个语义导向的对比学学习子网，其分别与区域类别和区域区域对之间形成对比。成对对比度任务利用从地面真理标签和预定义的类相似性分布派生的附加监督信号。在那些明确的语义监督的指导下，模型可以了解更多关于看不见的类别的知识，以避免看到概念的偏见问题，同时优化视觉功能的数据结构，以更好地辨别更好的视觉语义对齐。广泛的实验是在ZSD，即Pascal VOC和MS Coco的两个流行基准上进行的。结果表明，我们的方法优于ZSD和广义ZSD任务的先前最先进的。

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

尽管Yolov2方法在对象检测时非常快，但由于其骨干网络的性能较低和多尺度区域特征的缺乏，其检测准确性受到限制。因此，在本文中提出了一种基于Yolov2的Yolo（DC）Yolo（DC-SPP-YOLO）方法的密集连接（DC）和空间金字塔池（SPP）方法。具体而言，在Yolov2的骨干网络中采用了卷积层的密集连接，以增强特征提取并减轻消失的梯度问题。此外，引入了改进的空间金字塔池以池并加入多尺度区域特征，以便网络可以更全面地学习对象功能。 DC-SPP-YOLO模型是根据由MSE（均方误差）损耗和跨透镜损失组成的新损失函数建立和训练的。实验结果表明，DC-SPP-Yolo的地图（平均平均精度）高于Pascal VOC数据集和UA-Detrac数据集上的Yolov2。提出了DC-SPP-Yolo方法的有效性。

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.

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.

多标签图像识别是一个基本又实用的任务，因为真实世界的图像固有地拥有多个语义标签。然而，由于输入图像和输出标签空间的复杂性，难以收集大规模的多标签注释。为了降低注释成本，我们提出了一种结构化语义传输（SST）框架，使得能够培训具有部分标签的多标签识别模型，即，仅在每个图像中丢失其他标签（也称为未知标签）。该框架由两个互补传输模块组成，探索图像内和交叉图像语义相关性，以传输已知标签的知识，以为未知标签生成伪标签。具体地，一个图像内语义传输模块学习特定于图像的标签共出矩阵，并将已知的标签映射到基于该矩阵的补充未知标签。同时，交叉图像传输模块学习特定于类别的特征相似性，并帮助您具有高相似之处的补充未知标签。最后，已知和生成的标签都用于训练多标签识别模型。对Microsoft Coco，Visual Genome和Pascal VOC数据集的广泛实验表明，所提出的SST框架在当前最先进的算法上获得了卓越的性能。代码可用于\ url {https:/github.com/hcplab-sysu/sst-ml -pl

Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However, OOD detection in the multi-label classification task remains an underexplored domain. In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution) and irrelevant objects (e.g., OOD objects) on images that contain multiple objects from different categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.

很少有分段旨在学习一个细分模型，该模型可以推广到只有几个培训图像的新课程。在本文中，我们提出了一个交叉引用和局部全球条件网络（CRCNET），以进行几次分割。与以前仅预测查询图像掩码的作品不同，我们提出的模型同时对支持图像和查询图像进行了预测。我们的网络可以更好地在两个图像中使用交叉引用机制找到同时出现的对象，从而有助于少量分割任务。为了进一步改善功能比较，我们开发了一个局部全球条件模块，以捕获全球和本地关系。我们还开发了一个掩模修补模块，以重新完善前景区域的预测。Pascal VOC 2012，MS Coco和FSS-1000数据集的实验表明，我们的网络实现了新的最新性能。

我们提出对象盒，这是一种新颖的单阶段锚定且高度可推广的对象检测方法。与现有的基于锚固的探测器和无锚的探测器相反，它们更偏向于其标签分配中的特定对象量表，我们仅将对象中心位置用作正样本，并在不同的特征级别中平均处理所有对象，而不论对象'尺寸或形状。具体而言，我们的标签分配策略将对象中心位置视为形状和尺寸不足的锚定，并以无锚固的方式锚定，并允许学习每个对象的所有尺度。为了支持这一点，我们将新的回归目标定义为从中心单元位置的两个角到边界框的四个侧面的距离。此外，为了处理比例变化的对象，我们提出了一个量身定制的损失来处理不同尺寸的盒子。结果，我们提出的对象检测器不需要在数据集中调整任何依赖数据集的超参数。我们在MS-Coco 2017和Pascal VOC 2012数据集上评估了我们的方法，并将我们的结果与最先进的方法进行比较。我们观察到，与先前的作品相比，对象盒的性能优惠。此外，我们执行严格的消融实验来评估我们方法的不同组成部分。我们的代码可在以下网址提供：https：//github.com/mohsenzand/objectbox。

我们介绍了一种名为RobustAbnet的新表检测和结构识别方法，以检测表的边界并从异质文档图像中重建每个表的细胞结构。为了进行表检测，我们建议将Cornernet用作新的区域建议网络来生成更高质量的表建议，以更快的R-CNN，这显着提高了更快的R-CNN的定位准确性以进行表检测。因此，我们的表检测方法仅使用轻巧的RESNET-18骨干网络，在三个公共表检测基准（即CTDAR TRACKA，PUBLAYNET和IIIT-AR-13K）上实现最新性能。此外，我们提出了一种新的基于分裂和合并的表结构识别方法，其中提出了一个新型的基于CNN的新空间CNN分离线预测模块将每个检测到的表分为单元格，并且基于网格CNN的CNN合并模块是应用用于恢复生成细胞。由于空间CNN模块可以有效地在整个表图像上传播上下文信息，因此我们的表结构识别器可以坚固地识别具有较大的空白空间和几何扭曲（甚至弯曲）表的表。得益于这两种技术，我们的表结构识别方法在包括SCITSR，PubTabnet和CTDAR TrackB2-Modern在内的三个公共基准上实现了最先进的性能。此外，我们进一步证明了我们方法在识别具有复杂结构，大空间以及几何扭曲甚至弯曲形状的表上的表格上的优势。

大多数最先进的实例级人类解析模型都采用了两阶段的基于锚的探测器，因此无法避免启发式锚盒设计和像素级别缺乏分析。为了解决这两个问题，我们设计了一个实例级人类解析网络，该网络在像素级别上无锚固且可解决。它由两个简单的子网络组成：一个用于边界框预测的无锚检测头和一个用于人体分割的边缘引导解析头。无锚探测器的头继承了像素样的优点，并有效地避免了对象检测应用中证明的超参数的敏感性。通过引入部分感知的边界线索，边缘引导的解析头能够将相邻的人类部分与彼此区分开，最多可在一个人类实例中，甚至重叠的实例。同时，利用了精炼的头部整合盒子级别的分数和部分分析质量，以提高解析结果的质量。在两个多个人类解析数据集（即CIHP和LV-MHP-V2.0）和一个视频实例级人类解析数据集（即VIP）上进行实验，表明我们的方法实现了超过全球级别和实例级别的性能最新的一阶段自上而下的替代方案。

对象检测是计算机视觉和图像处理中的基本任务。基于深度学习的对象探测器非常成功，具有丰富的标记数据。但在现实生活中，它不保证每个对象类别都有足够的标记样本进行培训。当训练数据有限时，这些大型物体探测器易于过度装备。因此，有必要将几次拍摄的学习和零射击学习引入对象检测，这可以将低镜头对象检测命名在一起。低曝光对象检测（LSOD）旨在检测来自少数甚至零标记数据的对象，其分别可以分为几次对象检测（FSOD）和零拍摄对象检测（ZSD）。本文对基于深度学习的FSOD和ZSD进行了全面的调查。首先，本调查将FSOD和ZSD的方法分类为不同的类别，并讨论了它们的利弊。其次，本调查审查了数据集设置和FSOD和ZSD的评估指标，然后分析了在这些基准上的不同方法的性能。最后，本调查讨论了FSOD和ZSD的未来挑战和有希望的方向。

In recent years, object detection has achieved a very large performance improvement, but the detection result of small objects is still not very satisfactory. This work proposes a strategy based on feature fusion and dilated convolution that employs dilated convolution to broaden the receptive field of feature maps at various scales in order to address this issue. On the one hand, it can improve the detection accuracy of larger objects. On the other hand, it provides more contextual information for small objects, which is beneficial to improving the detection accuracy of small objects. The shallow semantic information of small objects is obtained by filtering out the noise in the feature map, and the feature information of more small objects is preserved by using multi-scale fusion feature module and attention mechanism. The fusion of these shallow feature information and deep semantic information can generate richer feature maps for small object detection. Experiments show that this method can have higher accuracy than the traditional YOLOv3 network in the detection of small objects and occluded objects. In addition, we achieve 32.8\% Mean Average Precision on the detection of small objects on MS COCO2017 test set. For 640*640 input, this method has 88.76\% mAP on the PASCAL VOC2012 dataset.

This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.

视频分析的图像分割在不同的研究领域起着重要作用，例如智能城市，医疗保健，计算机视觉和地球科学以及遥感应用。在这方面，最近致力于发展新的细分策略;最新的杰出成就之一是Panoptic细分。后者是由语义和实例分割的融合引起的。明确地，目前正在研究Panoptic细分，以帮助获得更多对视频监控，人群计数，自主驾驶，医学图像分析的图像场景的更细致的知识，以及一般对场景更深入的了解。为此，我们介绍了本文的首次全面审查现有的Panoptic分段方法，以获得作者的知识。因此，基于所采用的算法，应用场景和主要目标的性质，执行现有的Panoptic技术的明确定义分类。此外，讨论了使用伪标签注释新数据集的Panoptic分割。继续前进，进行消融研究，以了解不同观点的Panoptic方法。此外，讨论了适合于Panoptic分割的评估度量，并提供了现有解决方案性能的比较，以告知最先进的并识别其局限性和优势。最后，目前对主题技术面临的挑战和吸引不久的将来吸引相当兴趣的未来趋势，可以成为即将到来的研究研究的起点。提供代码的文件可用于：https：//github.com/elharroussomar/awesome-panoptic-egation

多标签图像分类允许从给定图像预测一组标签。与多类分类不同，每个图像只有一个标签，此类设置适用于更广泛的应用程序。在这项工作中，我们重新审视了多标签分类的两种流行方法：基于变压器的头和标签关系信息信息图处理分支。尽管基于变压器的头被认为比基于图基的分支更好地取得了更好的结果，但我们认为，使用适当的训练策略，基于图形的方法可以证明精确度的较小，同时将计算资源减少到推理上。在我们的训练策略中，我们在角度空间中引入了其修饰作用，而不是非对称损失（ASL）（ASL），而不是非对称损失（ASL）。与二进制跨熵损失相比，它隐含地学习了每个班级单位超球的代理特征向量，从而提供更好的歧视能力。根据提出的损失和训练策略，我们在单个模态方法中获得SOTA结果，以广泛的多标签分类基准，例如MS-Coco，Pascal-Voc，Nus wide和Visual Genome 500。 OpenVino培训扩展https://github.com/openvinotoolkit/deep-object-reid/tree/tree/multilabel

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. ...

Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. One common way to extract multi-scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixelwise classification. In this work, we propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location. We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model. The proposed attention model not only outperforms averageand max-pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi-scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL-Person-Part,