空间推理给智能代理带来了一个特殊的挑战，同时是他们在物理世界中成功互动和交流的先决条件。这样的推理任务是描述目标对象在通过相对方向的某些参考对象的固有方向方面的位置。在本文中，我们介绍了基于抽象对象的新型诊断视觉询问（VQA）数据集。我们的数据集允许对端到端VQA模型对地面相对方向的功能进行细粒度分析。同时，与现有数据集相比，模型培训需要少得多的计算资源，但产生可比甚至更高的性能。除了新数据集外，我们还基于在Grid-A-3D训练的两个端到端的VQA架构进行彻底评估。我们证明，在几个时期内，以相对方向进行推理所需的子任务，例如在场景中识别和定位对象并估算其内在方向，以直观的方式处理相对方向。

Knowledge about space and time is necessary to solve problems in the physical world: An AI agent situated in the physical world and interacting with objects often needs to reason about positions of and relations between objects; and as soon as the agent plans its actions to solve a task, it needs to consider the temporal aspect (e.g., what actions to perform over time). Spatio-temporal knowledge, however, is required beyond interacting with the physical world, and is also often transferred to the abstract world of concepts through analogies and metaphors (e.g., "a threat that is hanging over our heads"). As spatial and temporal reasoning is ubiquitous, different attempts have been made to integrate this into AI systems. In the area of knowledge representation, spatial and temporal reasoning has been largely limited to modeling objects and relations and developing reasoning methods to verify statements about objects and relations. On the other hand, neural network researchers have tried to teach models to learn spatial relations from data with limited reasoning capabilities. Bridging the gap between these two approaches in a mutually beneficial way could allow us to tackle many complex real-world problems, such as natural language processing, visual question answering, and semantic image segmentation. In this chapter, we view this integration problem from the perspective of Neuro-Symbolic AI. Specifically, we propose a synergy between logical reasoning and machine learning that will be grounded on spatial and temporal knowledge. Describing some successful applications, remaining challenges, and evaluation datasets pertaining to this direction is the main topic of this contribution.

Videos often capture objects, their visible properties, their motion, and the interactions between different objects. Objects also have physical properties such as mass, which the imaging pipeline is unable to directly capture. However, these properties can be estimated by utilizing cues from relative object motion and the dynamics introduced by collisions. In this paper, we introduce CRIPP-VQA, a new video question answering dataset for reasoning about the implicit physical properties of objects in a scene. CRIPP-VQA contains videos of objects in motion, annotated with questions that involve counterfactual reasoning about the effect of actions, questions about planning in order to reach a goal, and descriptive questions about visible properties of objects. The CRIPP-VQA test set enables evaluation under several out-of-distribution settings -- videos with objects with masses, coefficients of friction, and initial velocities that are not observed in the training distribution. Our experiments reveal a surprising and significant performance gap in terms of answering questions about implicit properties (the focus of this paper) and explicit properties of objects (the focus of prior work).

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

人类视觉感知的关键方面是能够将视觉场景分解为单个对象并进一步进入对象部分，形成部分整个层次结构。这种复合结构可以诱导丰富的语义概念和关系，从而在视觉信号的解释和组织中发挥着重要作用，以及视觉感知和推理的概括。但是，现有的视觉推理基准主要专注于物体而不是零件。基于完整的部分整个层次结构的视觉推理比以前粒度概念，更丰富的几何关系和更复杂的物理学所致的对象的推理更具挑战性。因此，为了更好地为基于部分的概念，关系和物理推理服务，我们介绍了一个名为PTR的新型大规模诊断视觉推理数据集。 PTR包含大约70k RGBD合成图像，具有地面真理对象和有关语义实例分段，颜色属性，空间和几何关系的部分级别注释，以及诸如稳定性的某些物理性质。这些图像与700K机生成的问题配对，涵盖各种类型的推理类型，使其成为视觉推理模型的良好测试平台。我们在这个数据集上检查了几种最先进的视觉推理模型，并观察到他们在人类可以容易地推断正确答案的情况下仍然存在许多令人惊讶的错误。我们认为，此数据集将开辟基于零件推理的新机会。

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages Visual Genome scene graph structures to create 22M diverse reasoning questions, which all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. A careful analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains a mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding of vision and language.

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings. Existing benchmarks for visual question answering can help, but have strong biases that models can exploit to correctly answer questions without reasoning. They also conflate multiple sources of error, making it hard to pinpoint model weaknesses. We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires. We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.

Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple factors of variation are intertwined, making generalization difficult to analyze. This motivates us to introduce a virtual benchmark, Super-CLEVR, where different factors in VQA domain shifts can be isolated in order that their effects can be studied independently. Four factors are considered: visual complexity, question redundancy, concept distribution and concept compositionality. With controllably generated data, Super-CLEVR enables us to test VQA methods in situations where the test data differs from the training data along each of these axes. We study four existing methods, including two neural symbolic methods NSCL and NSVQA, and two non-symbolic methods FiLM and mDETR; and our proposed method, probabilistic NSVQA (P-NSVQA), which extends NSVQA with uncertainty reasoning. P-NSVQA outperforms other methods on three of the four domain shift factors. Our results suggest that disentangling reasoning and perception, combined with probabilistic uncertainty, form a strong VQA model that is more robust to domain shifts. The dataset and code are released at https://github.com/Lizw14/Super-CLEVR.

我们介绍了CLEVR-MATH，这是一个多模式数学单词问题数据集，该数据集由涉及加法/减法的简单数学单词问题组成，部分地表示文本描述，部分地是由图像说明了场景。文本描述了图像中描述的场景上执行的动作。由于提出的问题可能与图像中的场景有关，而是针对采用动作之前或之后的场景状态，因此求解器设想或想象由于这些动作而导致的状态发生了变化。解决这些单词问题需要语言，视觉和数学推理的结合。我们将最新的神经和神经符号模型应用于CLEVR-MATH的视觉问题，并经验评估其表现。我们的结果表明，两种方法如何推广到操作链。我们讨论了两者在解决多模式单词问题解决的任务时的局限性。

Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn. In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning. We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamic physical systems. Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs. Our work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.

We present a new AI task -Embodied Question Answering (EmbodiedQA) -where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ('orange'). This challenging task requires a range of AI skills -active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

最近，3D视觉和语言任务吸引了不断增长的研究兴趣。与其他视觉和语言任务相比，3D视觉问题回答（VQA）任务的利用较小，并且更容易受到语言先验和共同参考的歧义。同时，由于规模和注释方法有限，最近提出的几个3D VQA数据集并不能很好地支持3D VQA任务。在这项工作中，我们通过收集一个新的3D VQA数据集（称为FE-3DGQA），正式定义和解决3D接地的VQA任务，并具有多样化且相对自由形式的提问，以及密集和完全接地的边界框注释。为了获得更多可解释的答案，我们标记了出现在复杂的质量检查对中的对象，该对象具有不同的语义类型，包括答案接地的对象（均出现并未出现在问题中），以及用于答案的对象的上下文对象。我们还提出了一个新的3D VQA框架，以有效地预测完全视觉扎根和可解释的答案。广泛的实验证明，我们新收集的基准数据集可有效地用于评估不同方面的各种3D VQA方法，而我们新提出的框架也可以在新的基准数据集中实现最新的性能。新收集的数据集和我们的代码都将在http://github.com/zlccccc/3dgqa上公开获得。

3D场景理解是一个相对新兴的研究领域。在本文中，我们介绍了3D现实世界场景（VQA-3D）中的视觉问题应答任务，旨在给出3D场景的所有可能的问题。为了解决这个问题，提出了第一个VQA-3D数据集，即CLEVR3D，其中包含在1,129个现实世界场景中的60k个问题。具体而言，我们开发一个问题发动机利用3D场景图结构来生成不同的推理问题，涵盖物体属性的问题（即，大小，颜色和材料）及其空间关系。建立在此数据集之上，我们进一步设计了第一个VQA-3D基线模型TransVQA3D。 TransVQA3D型号采用精心设计的变压器架构，实现优越的VQA-3D性能，与纯语言基线和先前的3D推理方法直接应用于3D场景。实验结果验证了VQA-3D作为辅助任务可以提高3D场景理解的性能，包括节点明智分类和全图识别的场景图分析。

服务机器人应该能够与非专家用户自然互动，不仅可以帮助他们完成各种任务，还可以接收指导，以解决指导中可能存在的歧义。我们考虑了视觉接地的任务，在这种情况下，代理将对象从拥挤的场景中分离出自然语言描述。现代的整体视觉接地方法通常忽略语言结构，而努力覆盖通用领域，因此很大程度上依靠大型数据集。此外，由于基准和目标域之间的高视觉差异，它们在RGB-D数据集中的传输性能受到了影响。模块化方法将学习与领域建模结合并利用语言的组成性质，以使视觉表示从语言解析中解脱出来，但由于缺乏强大的监督，要么依靠外部解析或以端到端的方式进行训练。在这项工作中，我们试图通过引入一个完全脱钩的模块化框架来解决这些局限性，以构成实体，属性和空间关系的组成视觉基础。我们利用在合成域中生成的丰富场景图表注释，并独立训练每个模块。我们的方法在模拟和两个真实的RGB-D场景数据集中进行了评估。实验结果表明，我们的框架的解耦性可以轻松地与域适应方法相结合，以实现SIMS到现实的视觉识别，从而为机器人应用中的视觉接地提供了数据效率，健壮且可解释的解决方案。

Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made even harder if it must be performed from a single image. We explore a controlled setting whereby questions are posed about the properties of a scene if that scene was observed from another viewpoint. To do this we have created a new version of the CLEVR dataset that we call CLEVR Mental Rotation Tests (CLEVR-MRT). Using CLEVR-MRT we examine standard methods, show how they fall short, then explore novel neural architectures that involve inferring volumetric representations of a scene. These volumes can be manipulated via camera-conditioned transformations to answer the question. We examine the efficacy of different model variants through rigorous ablations and demonstrate the efficacy of volumetric representations.

We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge. * Contributed equally. † Work done as an undergraduate at Cornell University. 1 In parts of this paper, we use the term compositional differently than it is commonly used in linguistics to refer to reasoning that requires composition. This type of reasoning often manifests itself in highly compositional language.2 Appendix G contains license information for all photographs used in this paper. 3 The top example is True, while the bottom is False.

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in

已经开发了许多Visio语言（V + L）表示学习方法，但现有数据集不会评估它们在统一空间中代表视觉和语言概念的程度。灵感来自于奇妙的转移和精神语言学文献，我们提出了一个新的V + L型号的评价设置：零射频跨模型转移。现有的V + L基准也经常在整个数据集上报告全局精度分数，渲染难以确定模型失败并成功的具体推理任务。要解决此问题并启用对跨模型传输的评估，我们存在TRAVLR，包括四个V + L推理任务的合成数据集。每个示例对场景进行了双倍，使得在训练/测试期间可以丢弃无论是没有相关信息的丢失。 Travlr的培训和测试分布也沿任务相关维度约束，从而可以评估分配外概括。我们评估了四个最先进的V + L型号，发现它们在从同一模态的测试集上表现良好，但所有型号都无法转移交叉模态，并且成功有限，容纳一个模态的添加或删除。在与事先工作的对齐中，我们还发现这些模型需要大量数据来学习简单的空间关系。我们将Travlr释放为研究界的开放挑战。

近年来，视觉问题应答（VQA）在近年来，由于了解来自多种方式的信息（即图像，语言），近年来近年来在近年来的机器学习社区中获得了很多牵引力。在VQA中，基于一组图像提出了一系列问题，并且手头的任务是到达答案。为实现这一目标，我们采用了一种基于象征的推理方法，使用正式逻辑框架。图像和问题被转换为执行显式推理的符号表示。我们提出了一种正式的逻辑框架，其中（i）图像在场景图的帮助下将图像转换为逻辑背景事实，（ii）问题被基于变压器的深度学习模型转换为一阶谓词逻辑条款，（iii）通过使用背景知识和谓词条款的接地来执行可靠性检查，以获得答案。我们所提出的方法是高度解释的，并且可以通过人容易地分析管道中的每个步骤。我们验证了我们在CLEVR和GQA数据集上的方法。我们在Clevr DataSet上实现了99.6％的近似完美的准确性，可与艺术模式相当，展示正式逻辑是一个可行的工具来解决视觉问题的回答。我们的模型也是数据高效，在仅在培训数据的10％培训时，在缩放数据集中实现99.1％的准确性。

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.