'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning about Actions & Change' (RAC). Recently, there has been growing interest in the study of RAC with visual and linguistic inputs. Graphs are often used to represent semantic structure of the visual content (i.e. objects, their attributes and relationships among objects), commonly referred to as scene-graphs. In this work, we propose a novel method that leverages scene-graph representation of images to reason about the effects of actions described in natural language. We experiment with existing CLEVR_HYP (Sampat et. al, 2021) dataset and show that our proposed approach is effective in terms of performance, data efficiency, and generalization capability compared to existing models.

'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning about Actions & Change' (RAC). This has been an important research direction in Artificial Intelligence (AI) in general, but the study of RAC with visual and linguistic inputs is relatively recent. The CLEVR_HYP (Sampat et. al., 2021) is one such testbed for hypothetical vision-language reasoning with actions as the key focus. In this work, we propose a novel learning strategy that can improve reasoning about the effects of actions. We implement an encoder-decoder architecture to learn the representation of actions as vectors. We combine the aforementioned encoder-decoder architecture with existing modality parsers and a scene graph question answering model to evaluate our proposed system on the CLEVR_HYP dataset. We conduct thorough experiments to demonstrate the effectiveness of our proposed approach and discuss its advantages over previous baselines in terms of performance, data efficiency, and generalization capability.

“行动”在人类与世界互动并使他们实现理想的目标方面起着至关重要的作用。结果，对人类的最常识（CS）知识围绕着行动。尽管“关于行动与变革的推理”（RAC）在知识代表社区中得到了广泛的研究，但它最近引起了NLP和计算机视觉研究人员的兴趣。本文调查了现有的任务，基准数据集，各种技术和模型，以及它们在视觉和语言领域中RAC中进步的各自绩效。最后，我们总结了我们的关键要点，讨论该研究领域面临的目前挑战，并概述了未来研究的潜在方向。

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.

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.

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。

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).

机器人社区早已期望在混乱环境中处理物体的能力。但是，大多数作品只是专注于操纵，而不是在混乱的对象中呈现隐藏的语义信息。在这项工作中，我们介绍了在混乱的场景中进行体现探索的场景图，以解决此问题。为了在混乱的情况下验证我们的方法，我们采用操纵问题答案（MQA）任务作为我们的测试基准，该测试基准要求具有体现的机器人具有主动探索能力和视觉和语言的语义理解能力。任务，我们提出了一种模仿学习方法，以生成探索的操作。同时，采用了基于动态场景图的VQA模型来理解操纵器手腕摄像头的一系列RGB帧以及操纵的每一步，以在我们的框架中回答问题。我们提出的框架对于MQA任务有效，代表了混乱的场景中的任务。

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.

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.

Visual question answering is fundamentally compositional in nature-a question like where is the dog? shares substructure with questions like what color is the dog? and where is the cat? This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning neural module networks, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.

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。

考虑到安全至关重要自动化系统中情境意识的功能，对驾驶场景的风险及其解释性的感知对于自主和合作驾驶特别重要。为了实现这一目标，本文提出了在驾驶场景中的共同风险定位的新研究方向及其作为自然语言描述的风险解释。由于缺乏标准基准，我们收集了一个大规模数据集，戏剧性（带有字幕模块的驾驶风险评估机制），该数据集由17,785个在日本东京收集的互动驾驶场景组成。我们的戏剧数据集适用于带有相关重要对象的驾驶风险的视频和对象级别的问题，以实现视觉字幕的目标，作为一种自由形式的语言描述，利用封闭式和开放式响应用于多层次问题，可以用来使用这些响应，可用于在驾驶场景中评估一系列视觉字幕功能。我们将这些数据提供给社区以进行进一步研究。使用戏剧，我们探索了在互动驾驶场景中的联合风险定位和字幕的多个方面。特别是，我们基准了各种多任务预测架构，并提供了关节风险定位和风险字幕的详细分析。数据集可在https://usa.honda-ri.com/drama上获得

在工厂或房屋等环境中协助我们的机器人必须学会使用对象作为执行任务的工具，例如使用托盘携带对象。我们考虑了学习常识性知识何时可能有用的问题，以及如何与其他工具一起使用其使用以完成由人类指示的高级任务。具体而言，我们引入了一种新型的神经模型，称为Tooltango，该模型首先预测要使用的下一个工具，然后使用此信息来预测下一项动作。我们表明，该联合模型可以告知学习精细的策略，从而使机器人可以顺序使用特定工具，并在使模型更加准确的情况下增加了重要价值。 Tooltango使用图神经网络编码世界状态，包括对象和它们之间的符号关系，并使用人类教师的演示进行了培训，这些演示是指导物理模拟器中的虚拟机器人的演示。该模型学会了使用目标和动作历史的知识来参加场景，最终将符号动作解码为执行。至关重要的是，我们解决了缺少一些已知工具的看不见的环境的概括，但是存在其他看不见的工具。我们表明，通过通过从知识库中得出的预训练的嵌入来增强环境的表示，该模型可以有效地将其推广到新的环境中。实验结果表明，在预测具有看不见对象的新型环境中模拟移动操纵器的成功符号计划时，至少48.8-58.1％的绝对改善对基准的绝对改善。这项工作朝着使机器人能够快速合成复杂任务的强大计划的方向，尤其是在新颖的环境中

我们提出了神经符号视觉对话框（NSVD），这是将深度学习和符号程序执行的第一种方法进行多轮视觉上的推理。 NSVD对视觉对话固有的两个关键挑战：长距离共同参考分辨率以及消失的问题效能绩效大大优于现有的纯粹连接主义方法。我们通过提出一个更现实，更严格的评估方案来演示后者，在计算准确性时，我们在其中为完整对话记录使用预测的答案。我们描述了我们模型的两个变体，并表明使用这种新方案，我们的最佳模型在CLEVR -DIALOG上的准确度达到99.72％ - 相对提高了10％以上的技术，而仅需培训的一部分即可数据。此外，我们证明了我们的神经符号模型具有更高的平均第一场失败回合，对不完整的对话历史更强大，并且不仅概括了对话的最多三倍的对话，而且还比在训练中看到的对话更长，而且还要看不见问题。类型和场景。

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

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。

我们提出了一项新的3D问题答案的3D空间理解任务（3D-QA）。在3D-QA任务中，模型从丰富的RGB-D室内扫描的整个3D场景接收视觉信息，并回答关于3D场景的给定文本问题。与VQA的2D答案不同，传统的2D-QA模型遭受了对对象对齐和方向的空间理解的问题，并且从3D-QA中的文本问题中失败了对象本地化。我们为3D-QA提出了一个名为ScanQA模型的3D-QA基线模型，其中模型从3D对象提案和编码的句子嵌入中获取融合描述符。该学习描述符将语言表达式与3D扫描的底层几何特征相关联，并促进3D边界框的回归以确定文本问题中的描述对象。我们收集了人类编辑的问题答案对，自由表格答案将接地为3D场景中的3D对象。我们的新ScanQA数据集包含来自Scannet DataSet的800个室内场景的超过41K问答对。据我们所知，ScanQA是第一个在3D环境中执行对象接地的问答的大规模工作。

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.

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.