This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several objects, often exhibiting visual relationships of varied complexities between them. These inter-object relationships provide strong contextual cues toward improving grounding performance compared to a traditional object query-only-based localization task. A scene graph is an efficient and structured way to represent all the objects and their semantic relationships in the image. In an attempt towards bridging these two modalities representing scenes and utilizing contextual information for improving object localization, we rigorously study the problem of grounding scene graphs on natural images. To this end, we propose a novel graph neural network-based approach referred to as Visio-Lingual Message PAssing Graph Neural Network (VL-MPAG Net). In VL-MPAG Net, we first construct a directed graph with object proposals as nodes and an edge between a pair of nodes representing a plausible relation between them. Then a three-step inter-graph and intra-graph message passing is performed to learn the context-dependent representation of the proposals and query objects. These object representations are used to score the proposals to generate object localization. The proposed method significantly outperforms the baselines on four public datasets.

深度学习技术导致了通用对象检测领域的显着突破，近年来产生了很多场景理解的任务。由于其强大的语义表示和应用于场景理解，场景图一直是研究的焦点。场景图生成（SGG）是指自动将图像映射到语义结构场景图中的任务，这需要正确标记检测到的对象及其关系。虽然这是一项具有挑战性的任务，但社区已经提出了许多SGG方法并取得了良好的效果。在本文中，我们对深度学习技术带来了近期成就的全面调查。我们审查了138个代表作品，涵盖了不同的输入方式，并系统地将现有的基于图像的SGG方法从特征提取和融合的角度进行了综述。我们试图通过全面的方式对现有的视觉关系检测方法进行连接和系统化现有的视觉关系检测方法，概述和解释SGG的机制和策略。最后，我们通过深入讨论当前存在的问题和未来的研究方向来完成这项调查。本调查将帮助读者更好地了解当前的研究状况和想法。

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. ...

场景图是一个场景的结构化表示，可以清楚地表达场景中对象之间的对象，属性和关系。随着计算机视觉技术继续发展，只需检测和识别图像中的对象，人们不再满足。相反，人们期待着对视觉场景更高的理解和推理。例如，给定图像，我们希望不仅检测和识别图像中的对象，还要知道对象之间的关系（视觉关系检测），并基于图像内容生成文本描述（图像标题）。或者，我们可能希望机器告诉我们图像中的小女孩正在做什么（视觉问题应答（VQA）），甚至从图像中移除狗并找到类似的图像（图像编辑和检索）等。这些任务需要更高水平的图像视觉任务的理解和推理。场景图只是场景理解的强大工具。因此，场景图引起了大量研究人员的注意力，相关的研究往往是跨模型，复杂，快速发展的。然而，目前没有对场景图的相对系统的调查。为此，本调查对现行场景图研究进行了全面调查。更具体地说，我们首先总结了场景图的一般定义，随后对场景图（SGG）和SGG的发电方法进行了全面和系统的讨论，借助于先验知识。然后，我们调查了场景图的主要应用，并汇总了最常用的数据集。最后，我们对场景图的未来发展提供了一些见解。我们相信这将是未来研究场景图的一个非常有帮助的基础。

Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel endto-end model that generates such structured scene representation from an input image. The model solves the scene graph inference problem using standard RNNs and learns to iteratively improves its predictions via message passing. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods for generating scene graphs using Visual Genome dataset and inferring support relations with NYU Depth v2 dataset.

We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics.

We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual Genome dataset. Our analysis shows that object labels are highly predictive of relation labels but not vice-versa. We also find that there are recurring patterns even in larger subgraphs: more than 50% of graphs contain motifs involving at least two relations. Our analysis motivates a new baseline: given object detections, predict the most frequent relation between object pairs with the given labels, as seen in the training set. This baseline improves on the previous state-of-the-art by an average of 3.6% relative improvement across evaluation settings. We then introduce Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graphs that further improves over our strong baseline by an average 7.1% relative gain. Our code is available at github.com/rowanz/neural-motifs.

同一场景中的不同对象彼此之间或多或少相关，但是只有有限数量的这些关系值得注意。受到对象检测效果的DETR的启发，我们将场景图生成视为集合预测问题，并提出了具有编码器decoder架构的端到端场景图生成模型RELTR。关于视觉特征上下文的编码器原因是，解码器使用带有耦合主题和对象查询的不同类型的注意机制渗透了一组固定大小的三胞胎主题prodicate-object。我们设计了一套预测损失，以执行地面真相与预测三胞胎之间的匹配。与大多数现有场景图生成方法相反，Reltr是一种单阶段方法，它仅使用视觉外观直接预测一组关系，而无需结合实体并标记所有可能的谓词。视觉基因组和开放图像V6数据集的广泛实验证明了我们模型的出色性能和快速推断。

Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach. This work introduces a generative transformer-based approach to generating scene graphs beyond link prediction. Using two transformer-based components, we first sample a possible scene graph structure from detected objects and their visual features. We then perform predicate classification on the sampled edges to generate the final scene graph. This approach allows us to efficiently generate scene graphs from images with minimal inference overhead. Extensive experiments on the Visual Genome dataset demonstrate the efficiency of the proposed approach. Without bells and whistles, we obtain, on average, 20.7% mean recall (mR@100) across different settings for scene graph generation (SGG), outperforming state-of-the-art SGG approaches while offering competitive performance to unbiased SGG approaches.

场景图生成（SGG）由于其复杂的成分特性，仍然是一个具有挑战性的视觉理解任务。大多数以前的作品采用自下而上的两阶段或基于点的单阶段方法，通常遭受开销时间复杂性或次优设计假设。在这项工作中，我们提出了一种新颖的SGG方法来解决上述问题，其将任务制定为双层图形施工问题。为了解决问题，我们开发一个基于变换器的端到端框架，首先生成实体和谓词提议集，然后推断定向边缘以形成关系三态。特别地，我们基于结构谓词发生器开发新的实体感知谓词表示，以利用关系的组成特性。此外，我们设计了一个曲线图组装模块，以推断基于我们的实体感知结构的二分明场景图的连接，使我们能够以端到端的方式生成场景图。广泛的实验结果表明，我们的设计能够在两个具有挑战性的基准上实现最先进的或可比性的性能，超越大多数现有方法，并享受更高的推理效率。我们希望我们的模型可以作为基于变压器的场景图生成的强大基线。

当代视觉标题模型通常是幻觉的对象，其实际上并不是一种场景，因为目视错误分类或过度依赖导致视觉信息与目标词汇词之间的语义不一致。最常见的方式是鼓励标题模型将生成的对象字或短语动态链接到图像的适当区域，即接地图像标题（GIC）。然而，GIC利用辅助任务（接地对象），这些任务（接地对象）没有解决对象幻觉的关键问题，即语义不一致。在本文中，我们对上面的问题进行了一种小说 - 利用视觉和语言模式之间的语义一致性。具体而言，我们提出了与GIC的共识RRAPH表示学习框架（CGRL），其纳入接地标题管道的共识表示。通过将可视图（例如，场景图）对准到图表中的节点和边的语言图来学习共识。通过对齐的共识，标题模型可以捕获正确的语言特征和视觉相关性，然后进一步接地适当的图像区域。我们验证了我们模型的有效性，对象幻觉（-9％主席）在Flickr30k实体数据集中显着下降。此外，我们的CGR还通过多种自动度量和人体评估评估，结果表明，该方法可以同时提高图像标题（+2.9苹果酒）和接地的性能（+2.3 f1loc）。

在图像理解项目中越来越多的情况下，场景图一代在电脑视觉研究中获得了很多关注，如视觉问题应答，图像标题，自动驾驶汽车，人群行为分析，活动识别等等。场景图，图像的视觉图形结构，非常有助于简化图像理解任务。在这项工作中，我们介绍了一个称为几何上下文的后处理算法，以了解视觉场景更好的几何上。我们使用该后处理算法在对象对与先前模型之间添加和改进几何关系。我们通过计算对象对之间的方向和距离来利用此上下文。我们使用知识嵌入式路由网络（KERN）作为我们的基准模型，将工作与我们的算法扩展，并显示最近最先进的算法上的可比结果。

This paper develops a novel framework for semantic image retrieval based on the notion of a scene graph. Our scene graphs represent objects ("man", "boat"), attributes of objects ("boat is white") and relationships between objects ("man standing on boat"). We use these scene graphs as queries to retrieve semantically related images. To this end, we design a conditional random field model that reasons about possible groundings of scene graphs to test images. The likelihoods of these groundings are used as ranking scores for retrieval. We introduce a novel dataset of 5,000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval. In particular, we evaluate retrieval using full scene graphs and small scene subgraphs, and show that our method outperforms retrieval methods that use only objects or low-level image features. In addition, we show that our full model can be used to improve object localization compared to baseline methods.

The prevailing framework for matching multimodal inputs is based on a two-stage process: 1) detecting proposals with an object detector and 2) matching text queries with proposals. Existing two-stage solutions mostly focus on the matching step. In this paper, we argue that these methods overlook an obvious \emph{mismatch} between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i.e., query-aware). Due to this mismatch, chances are that proposals relevant to the text query are suppressed during the filtering process, which in turn bounds the matching performance. To this end, we propose VL-NMS, which is the first method to yield query-aware proposals at the first stage. VL-NMS regards all mentioned instances as critical objects, and introduces a lightweight module to predict a score for aligning each proposal with a critical object. These scores can guide the NMS operation to filter out proposals irrelevant to the text query, increasing the recall of critical objects, resulting in a significantly improved matching performance. Since VL-NMS is agnostic to the matching step, it can be easily integrated into any state-of-the-art two-stage matching methods. We validate the effectiveness of VL-NMS on two multimodal matching tasks, namely referring expression grounding and image-text matching. Extensive ablation studies on several baselines and benchmarks consistently demonstrate the superiority of VL-NMS.

Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

3D密集字幕是最近提供的新型任务，其中点云包含比2D对应物更多的几何信息。但是，由于点云中包含的更高复杂性和更广泛的对象关系，它也更具挑战性。现有方法仅将这种关系视为图表中对象特征学习的副产品，而无需特别编码它们，从而导致了亚最佳结果。在本文中，旨在通过捕获和利用3D场景中的复杂关系来改善3D密集的字幕，我们提出了更多的多阶关系挖掘模型，以支持产生更多的描述性和全面标题。从技术上讲，我们更多地以渐进的方式编码对象关系，因为可以从有限数量的基本关系中推论复杂的关系。我们首先设计了一种新型的空间布局图卷积（SLGC），该图形将几个一阶关系编码为在3D对象建议上构造的图的边缘。接下来，从结果图中，我们进一步提取多个三重态，这些三重态将基本的一阶关系封装为基本单元，并构造几个以对象为中心的三重态注意图（OTAG），以推断每个目标对象的多阶关系。将OTAG的更新的节点功能聚合并输入标题解码器，以提供丰富的关系提示，因此可以生成包括与上下文对象的不同关系的字幕。 SCAN2CAP数据集的广泛实验证明了我们提出的更多及其组件的有效性，并且我们也表现优于当前最新方法。我们的代码可从https://github.com/sxjyjay/more获得。

最近，越来越多的努力集中在弱监督的场景图（WSSGG）上。 WSSGG的主流解决方案通常遵循相同的管道：它们首先将文本实体与弱图像级别的监督（例如，未定位的关系三胞胎或字幕）相结合，然后用图像区域对齐，然后以完全固定的实例训练SGG模型 - 级别的“伪”标签。但是，我们认为大多数现有的WSSGG仅专注于对象一致性，这意味着接地区域应具有与文本实体相同的对象类别标签。尽管他们忽略了理想对齐的另一个基本要求：相互作用，这意味着接地区域对应具有与文本实体对相同的相互作用（即视觉关系）。因此，在本文中，我们建议通过使用对象感知和互动感知知识来增强简单的接地模块，以获取更可靠的伪标签。为了更好地利用这两种类型的知识，我们将它们视为两位老师，并融合其生成的目标，以指导我们接地模块的训练过程。具体而言，我们设计了两种不同的策略，可以通过评估每个培训样本的可靠性来适应不同的教师。广泛的实验表明，我们的方法始终在各种弱监督下提高WSSGG性能。

The goal of this paper is to detect objects by exploiting their interrelationships. Rather than relying on predefined and labeled graph structures, we infer a graph prior from object co-occurrence statistics. The key idea of our paper is to model object relations as a function of initial class predictions and co-occurrence priors to generate a graph representation of an image for improved classification and bounding box regression. We additionally learn the object-relation joint distribution via energy based modeling. Sampling from this distribution generates a refined graph representation of the image which in turn produces improved detection performance. Experiments on the Visual Genome and MS-COCO datasets demonstrate our method is detector agnostic, end-to-end trainable, and especially beneficial for rare object classes. What is more, we establish a consistent improvement over object detectors like DETR and Faster-RCNN, as well as state-of-the-art methods modeling object interrelationships.

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.