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).

Video Question Answering methods focus on commonsense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the ``NewsVideoQA'' dataset that comprises more than $8,600$ QA pairs on $3,000+$ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.

视觉表示学习在各种现实世界中无处不在，包括视觉理解，视频理解，多模式分析，人类计算机的互动和城市计算。由于出现了大量多模式的异质空间/时间/时空数据，因此在大数据时代，缺乏可解释性，鲁棒性和分布外的概括正在成为现有视觉模型的挑战。大多数现有方法倾向于符合原始数据/可变分布，而忽略了多模式知识背后的基本因果关系，该知识缺乏统一的指导和分析，并分析了为什么现代视觉表示学习方法很容易崩溃成数据偏见并具有有限的概括和认知能力。因此，受到人类水平代理人的强大推理能力的启发，近年来见证了巨大的努力，以发展因果推理范式，以良好的认知能力实现强大的代表性和模型学习。在本文中，我们对视觉表示学习的现有因果推理方法进行了全面审查，涵盖了基本理论，模型和数据集。还讨论了当前方法和数据集的局限性。此外，我们提出了一些预期的挑战，机会和未来的研究方向，用于基准视觉表示学习中的因果推理算法。本文旨在为这个新兴领域提供全面的概述，引起人们的注意，鼓励讨论，使发展新颖的因果推理方法，公开可用的基准和共识建设标准的紧迫性，以可靠的视觉表示和相关的真实实践。世界应用更有效。

'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.

Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer.Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and highquality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (∼45%).To move towards cognition-level understanding, we present a new reasoning engine, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. R2C helps narrow the gap between humans and machines (∼65%); still, the challenge is far from solved, and we provide analysis that suggests avenues for future work.

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。

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。

现有的视觉问题回答方法倾向于捕获视觉和语言方式中的虚假相关性，并且未能发现真正的休闲机制，这些机制是基于主导的视觉证据和正确的问题意图而实现推理的真正休闲机制。此外，现有方法通常忽略了多模式设置中复杂的事件级别的理解，这需要因果推断对共同模型跨模式事件的时间性，因果关系和动力学的强大认知能力。在这项工作中，我们通过引入因果干预方法来减轻虚假相关性并发现真实的因果结构，从而从新的角度（即跨模式因果关系推理）回答事件级别的视觉问题，即跨模式的因果关系推理并发现了真实的因果结构，以集成视觉和语言的相关性方式。具体而言，我们提出了一个新颖的事件级视觉问题答案框架，称为跨模式因果关系推理（CMCIR），以实现强大的偶然性随意感知的视觉视觉语言问题。为了揭示视觉和语言方式的因果结构，提出了新颖的因果关系 - 感知视觉语言推理（CVLR）模块，以通过精心设计的前对门和后门Causal Causal Intervention模块进行合作地解散视觉和语言的杂语相关性。为了发现语言语义和时空表示之间的细粒度相互作用，我们构建了一种新型的时空变压器（STT），该变压器（STT）构建了视觉内容和语言内容之间的多模式共发生相互作用。大规模事件级城市数据集SUTD-TrafficQA和三个基准现实世界数据集TGIF-QA，MSVD-QA和MSRVTT-QA进行了广泛的实验，这证明了我们的CMCIR在发现视觉效果的Causal Causal Causal结构中的有效性。

“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。

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.

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.

为了使AI安全地在医院，学校和工作场所等现实世界中安全部署，它必须能够坚定地理解物理世界。这种推理的基础是物理常识：了解可用对象的物理特性和提供的能力，如何被操纵以及它们如何与其他对象进行交互。物理常识性推理从根本上是一项多感官任务，因为物理特性是通过多种模式表现出来的，其中两个是视觉和声学。我们的论文通过贡献PACS来朝着现实世界中的物理常识推理：第一个用于物理常识属性注释的视听基准。 PACS包含13,400对答案对，涉及1,377个独特的物理常识性问题和1,526个视频。我们的数据集提供了新的机会来通过将音频作为此多模式问题的核心组成部分来推进物理推理的研究领域。使用PACS，我们在我们的新挑战性任务上评估了多种最先进的模型。尽管某些模型显示出令人鼓舞的结果（精度为70％），但它们都没有人类的绩效（精度为95％）。我们通过证明多模式推理的重要性并为未来的研究提供了可能的途径来结束本文。

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。

问题回答（QA）是最重要的自然语言处理（NLP）任务之一。它旨在使用NLP技术根据大规模的非结构化语料库生成对给定问题的相应答案。随着深度学习的发展，正在提出越来越具有挑战性的质量检查数据集，并且许多用于解决它们的新方法也正在出现。在本文中，我们研究了在深度学习时代发布的有影响力的质量检查数据集。具体来说，我们首先引入两个最常见的质量检查任务 - 文本问题答案和视觉问题 - 分别涵盖最具代表性的数据集，然后给出质量检查研究的一些当前挑战。

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.

视觉问题回答（VQA）近年来见证了巨大进展。但是，大多数努力只关注2D图像问题应答任务。在本文中，我们介绍了将VQA扩展到3D域的第一次尝试，这可以促进人工智能对3D现实世界情景的看法。与基于图像的VQA不同，3D问题应答（3DQA）将颜色点云作为输入，需要外观和3D几何理解能力来回答3D相关问题。为此，我们提出了一种基于新颖的基于变换器的3DQA框架\ TextBF {“3DQA-TR”}，其包括两个编码器，分别用于利用外观和几何信息。外观，几何和的多模码信息语言问题最终可以通过3D语言伯特互相参加，以预测目标答案。要验证我们提出的3DQA框架的有效性，我们还开发了第一个建立的3DQA DataSet \ TextBF {“scanqa”} SCANNet DataSet并包含$ \ SIM $ 6K问题，$ \ SIM $ 30k答案，可满足806美元的场景。在此数据集上的广泛实验展示了我们提出的3DQA框架在现有的VQA框架上的明显优势，以及我们主要设计的有效性。我们的代码和数据集将公开可用于促进此方向的研究。

目前的视觉问题应答（VQA）任务主要考虑回答自然图像的人为注释问题。然而，除了自然图像之外，在视觉理解和推理研究中仍然可以解读具有语义丰富性的抽象图。在这项工作中，我们介绍了ICON问题的新挑战（ICONQA），其目标是在图标图像上下文中回答问题。我们发布了ICONQA，这是一个由107,439个问题和三个子任务组成的大型数据集：多图像选择，多文本选择和填充空白。 ICONQA数据集是由真实世界图中的启发，突出了抽象图理解和综合认知推理的重要性。因此，ICONQA不仅需要对象识别和文本理解等感知技能，而且还需要多种认知推理技能，例如几何推理，致辞推理和算术推理。为了促进潜在的iconqa模型来学习图标图像的语义表示，我们进一步发布了一个图标数据集图标645，其中包含377级上的645,687个彩色图标。我们进行广泛的用户研究和盲目实验，并重现各种先进的VQA方法来基准iconQA任务。此外，我们开发了一个强大的ICONQA基线Patch-TRM，它应用金字塔跨模型变压器，其中包含在图标数据集上预先培训的输入图嵌入式。 iconqa和图标645可在https://iconqa.github.io提供。

视觉问题的视觉关注在视觉问题上应答（VQA）目标在定位有关答案预测的右图像区域，提供强大的技术来促进多模态理解。然而，最近的研究指出，来自视觉关注的突出显示的图像区域通常与给定的问题和答案无关，导致模型混淆正确的视觉推理。为了解决这个问题，现有方法主要是为了对准人类关注的视觉注意力。尽管如此，收集这种人类数据是费力且昂贵的，使其在数据集中调整良好开发的模型。为了解决这个问题，在本文中，我们设计了一种新的视觉关注正规化方法，即attreg，以便在VQA中更好地视觉接地。具体而言，attraT首先识别了由骨干模型出乎意料地忽略（即，分配低注意重量）的问题所必需的图像区域。然后，利用掩模引导的学习方案来规范视觉注意力，以便更多地关注这些忽略的关键区域。所提出的方法是非常灵活的，模型不可知，可以集成到基于大多数基于视觉关注的VQA模型中，并且不需要人类注意监督。已经进行了三个基准数据集，即VQA-CP V2，VQA-CP V1和VQA V2的广泛实验，以评估attreg的有效性。作为副产品，将Attreg纳入强基线LMH时，我们的方法可以实现新的最先进的准确性为60.00％，在VQA-CP V2基准数据集上绝对性能增益为7.01％。 。

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.

视觉问题应答（VQA）是一个具有挑战性的任务，在计算机视觉和自然语言处理领域中引起了越来越多的关注。然而，目前的视觉问题回答具有语言偏差问题，这减少了模型的稳健性，对视觉问题的实际应用产生了不利影响。在本文中，我们首次对该领域进行了全面的审查和分析，并根据三个类别对现有方法进行分类，包括增强视觉信息，弱化语言前瞻，数据增强和培训策略。与此同时，依次介绍相关的代表方法，依次汇总和分析。揭示和分类语言偏见的原因。其次，本文介绍了主要用于测试的数据集，并报告各种现有方法的实验结果。最后，我们讨论了该领域的可能的未来研究方向。