第一人称视频在其持续环境的背景下突出了摄影师的活动。但是，当前的视频理解方法是从短视频剪辑中的视觉特征的原因，这些视频片段与基础物理空间分离，只捕获直接看到的东西。我们提出了一种方法，该方法通过学习摄影师（潜在看不见的）本地环境来促进以人为中心的环境的了解来链接以自我为中心的视频和摄像机随着时间的推移而张开。我们使用来自模拟的3D环境中的代理商的视频进行训练，在该环境中，环境完全可以观察到，并在看不见的环境的房屋旅行的真实视频中对其进行测试。我们表明，通过将视频接地在其物理环境中，我们的模型超过了传统的场景分类模型，可以预测摄影师所处的哪个房间（其中帧级信息不足），并且可以利用这种基础来定位与环境相对应的视频瞬间 - 中心查询，优于先验方法。项目页面：http：//vision.cs.utexas.edu/projects/ego-scene-context/

鉴于从第一人称角度捕获的视频以及录制视频的环境环境，我们可以认识到该人在做什么并确定3D空间中的动作发生在哪里吗？我们解决了这个具有挑战性的问题，即在以自我为中心视频的已知3D地图上共同识别和本地化操作。为此，我们提出了一种新颖的深层概率模型。我们的模型采用了3D环境的层次体积表示（HVR）的输入和以自我为中心的视频，将3D Action位置视为潜在变量，并根据其潜在位置的视频和上下文提示识别动作。为了评估我们的模型，我们对EGO4D数据集的子集进行了广泛的实验，其中捕获了人类自然主义的作用和照片现实的3D环境重建。我们的方法证明了在可见和看不见的环境之间进行动作识别和3D动作定位的强劲结果。我们认为，我们的工作指向以自我为中心的视觉和3D场景理解的相交的令人兴奋的研究方向。

Training effective embodied AI agents often involves manual reward engineering, expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization. Another approach is to use neural architectures alongside self-supervised objectives which encourage better representation learning. In practice, there are few guarantees that these self-supervised objectives encode task-relevant information. We propose the Scene Graph Contrastive (SGC) loss, which uses scene graphs as general-purpose, training-only, supervisory signals. The SGC loss does away with explicit graph decoding and instead uses contrastive learning to align an agent's representation with a rich graphical encoding of its environment. The SGC loss is generally applicable, simple to implement, and encourages representations that encode objects' semantics, relationships, and history. Using the SGC loss, we attain significant gains on three embodied tasks: Object Navigation, Multi-Object Navigation, and Arm Point Navigation. Finally, we present studies and analyses which demonstrate the ability of our trained representation to encode semantic cues about the environment.

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

最近的动作识别模型通过整合对象，其位置和互动来取得令人印象深刻的结果。但是，为每个框架获得密集的结构化注释是乏味且耗时的，使这些方法的训练昂贵且可扩展性较低。同时，如果可以在感兴趣的域内或之外使用一小部分带注释的图像，我们如何将它们用于下游任务的视频？我们提出了一个学习框架的结构（简称SVIT），该结构证明了仅在训练过程中仅可用的少量图像的结构才能改善视频模型。 SVIT依靠两个关键见解。首先，由于图像和视频都包含结构化信息，因此我们用一组\ emph {对象令牌}丰富了一个可以在图像和视频中使用的\ emph {对象令牌}的模型。其次，视频中各个帧的场景表示应与静止图像的场景表示“对齐”。这是通过\ emph {frame-clip一致性}损失来实现的，该损失可确保图像和视频之间结构化信息的流动。我们探索场景结构的特定实例化，即\ emph {手对象图}，由手和对象组成，其位置为节点，以及触点/no-contact的物理关系作为边缘。 SVIT在多个视频理解任务和数据集上显示出强烈的性能改进；它在EGO4D CVPR'22对象状态本地化挑战中赢得了第一名。对于代码和预算模型，请访问\ url {https://eladb3.github.io/svit/}的项目页面

在本文中，我们提出了一种新的方法来增强从单个可佩戴相机捕获的视频计算的人的3D身体姿势估计。关键的想法是利用在联合嵌入空间中链接第一和第三次视图的高级功能。为了了解这样的嵌入空间，我们介绍了First2第三姿势，这是一个近2,000个视频的新配对同步数据集，描绘了从第一和第三视角捕获的人类活动。我们明确地考虑了空间和运动域功能，同时使用以自我监督的方式培训的半暹罗架构。实验结果表明，使用我们的数据集学习的联合多视图嵌入式空间可用于从任意单视图的自拍视频中提取歧视特征，而无需需要域适应，也不知道相机参数。在三种监督最先进的方法中，我们在两个无约束数据集中实现了重大改善了两个无约束的数据集。我们的数据集和代码将可用于研究目的。

Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter.

互动对象理解，或者我们可以对对象做些什么以及计算机愿景的长期目标。在本文中，我们通过观察野外的自我高端视频的人类手来解决这个问题。我们展示了观察人类的手与之交互以及如何提供相关数据和必要的监督。参加双手，容易定位并稳定积极的物体以进行学习，并揭示发生与对象的交互的地方。分析手显示我们可以对物体做些什么以及如何做些。我们在史诗厨房数据集上应用这些基本原则，并成功地学习了国家敏感的特征，以及互动区域和提供了麦克拉斯的地区），纯粹是通过观察在EGoCentric视频中的手。

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 21 CoW baselines across Habitat, RoboTHOR, and Pasture. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions, but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration -- and no additional training -- matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on Habitat MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art RoboTHOR ZSON model.

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.

房间冲动响应（RIR）函数捕获周围的物理环境如何改变听众听到的声音，对AR，VR和机器人技术中的各种应用产生影响。估计RIR的传统方法在整个环境中采用密集的几何形状和/或声音测量值，但我们探讨了如何根据空间中观察到的一组稀疏图像和回声来推断RIR。为了实现这一目标，我们介绍了一种基于变压器的方法，该方法使用自我注意力来构建丰富的声学环境，然后通过跨注意来预测任意查询源接收器位置的河流。此外，我们设计了一个新颖的训练目标，该目标改善了RIR预测与目标之间的声学​​特征中的匹配。在使用3D环境的最先进的视听模拟器的实验中，我们证明了我们的方法成功地生成了任意RIR，优于最先进的方法，并且在与传统方法的主要背离中 - 以几种方式概括新的环境。项目：http：//vision.cs.utexas.edu/projects/fs_rir。

双耳音频提供具有沉浸式空间声音体验的人类听众，但大多数现有视频缺乏双耳录音。我们提出了一种音频空间化方法，它借鉴视频中的视觉信息，以将其单声道（单通道）音频转换为双耳音频。现有方法利用直接从视频帧提取的可视化功能，我们的方法明确地解除了视觉流中存在的几何线索以指导学习过程。特别是，我们开发了一种多任务框架，通过考虑底层室脉冲响应，从而为底层室的脉冲响应而学习几何感知功能，从声音源的位置，以及声音几何形状的一致性随着时间的推移。此外，我们介绍了一个新的大型视频数据集，具有逼真的双链条音频，用于真实世界扫描环境。在两个数据集上，我们展示了我们方法的功效，这实现了最先进的结果。

尽管当前的视觉算法在许多具有挑战性的任务上都表现出色，但尚不清楚他们如何理解现实世界环境的物理动态。在这里，我们介绍了Physion，一种数据集和基准，用于严格评估预测物理场景如何随着时间而发展的能力。我们的数据集具有对各种物理现象的现实模拟，包括刚性和软体体碰撞，稳定的多对象配置，滚动，滑动和弹丸运动，因此比以前的基准提供了更全面的挑战。我们使用Physion来基准一套模型，其体系结构，学习目标，投入输出结构和培训数据各不相同。同时，我们在同一场景上获得了人类预测行为的精确测量，从而使我们能够直接评估任何模型能够近似人类行为的效果。我们发现，学习以对象为中心的表示的视觉算法通常优于那些没有人的表现，但仍未达到人类绩效。另一方面，绘制具有直接访问物理状态信息的神经网络的表现效果更好，并且做出与人类制作的预测更相似。这些结果表明，提取场景的物理表征是在视力算法中实现人类水平和类似人类的物理理解的主要瓶颈。我们已公开发布了所有数据和代码，以促进使用物理以完全可重现的方式对其他模型进行基准测试，从而使对视觉算法的进度进行系统的评估，这些算法像人们一样坚固地了解物理环境。

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains $\unicode{x2014}$ Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations.

我们介绍重做，一个类无话的框架来重建RGBD或校准视频的动态对象。与事先工作相比，我们的问题设置是更真实的，更具挑战性的三个原因：1）由于遮挡或相机设置，感兴趣的对象可能永远不会完全可见，但我们的目标是重建完整的形状; 2）我们的目标是处理不同的对象动态，包括刚性运动，非刚性运动和关节; 3）我们的目标是通过一个统一的框架重建不同类别的对象。为了解决这些挑战，我们开发了两种新模块。首先，我们介绍了一个规范的4D隐式功能，它是与聚合的时间视觉线索对齐的像素对齐。其次，我们开发了一个4D变换模块，它捕获对象动态以支持时间传播和聚合。我们研究了重做在综合性RGBD视频数据集风帆-VOS 3D和Deformingthings4d ++上的大量实验中的疗效，以及现实世界视频数据3DPW。我们发现重做优于最先进的动态重建方法。在消融研究中，我们验证每个发达的组件。

在视觉和语言导航（VLN）中，按照自然语言指令在现实的3D环境中需要具体的代理。现有VLN方法的一个主要瓶颈是缺乏足够的培训数据，从而导致对看不见的环境的概括不令人满意。虽然通常会手动收集VLN数据，但这种方法很昂贵，并且可以防止可扩展性。在这项工作中，我们通过建议从HM3D自动创建900个未标记的3D建筑物的大规模VLN数据集来解决数据稀缺问题。我们为每个建筑物生成一个导航图，并通过交叉视图一致性从2D传输对象预测，从2D传输伪3D对象标签。然后，我们使用伪对象标签来微调一个预处理的语言模型，作为减轻教学生成中跨模式差距的提示。在导航环境和说明方面，我们生成的HM3D-AUTOVLN数据集是比现有VLN数据集大的数量级。我们通过实验表明，HM3D-AUTOVLN显着提高了所得VLN模型的概括能力。在SPL指标上，我们的方法分别在Reverie和DataSet的看不见的验证分裂分别对艺术的状态提高了7.1％和8.1％。

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.

Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as gains on long-tail object queries, and the ability to perform zero-shot and few-shot NLQ.

A household robot should be able to navigate to target locations without requiring users to first annotate everything in their home. Current approaches to this object navigation challenge do not test on real robots and rely on expensive semantically labeled 3D meshes. In this work, our aim is an agent that builds self-supervised models of the world via exploration, the same as a child might. We propose an end-to-end self-supervised embodied agent that leverages exploration to train a semantic segmentation model of 3D objects, and uses those representations to learn an object navigation policy purely from self-labeled 3D meshes. The key insight is that embodied agents can leverage location consistency as a supervision signal - collecting images from different views/angles and applying contrastive learning to fine-tune a semantic segmentation model. In our experiments, we observe that our framework performs better than other self-supervised baselines and competitively with supervised baselines, in both simulation and when deployed in real houses.

我们介绍了在视频中发现时间精确，细粒度事件的任务（检测到时间事件的精确时刻）。精确的斑点需要模型在全球范围内对全日制动作规模进行推理，并在本地识别微妙的框架外观和运动差异，以识别这些动作过程中事件的识别。令人惊讶的是，我们发现，最高的绩效解决方案可用于先前的视频理解任务，例如操作检测和细分，不能同时满足这两个要求。作为响应，我们提出了E2E点，这是一种紧凑的端到端模型，在精确的发现任务上表现良好，可以在单个GPU上快速培训。我们证明，E2E点的表现明显优于最近根据视频动作检测，细分和将文献发现到精确的发现任务的基线。最后，我们为几个细粒度的运动动作数据集贡献了新的注释和分裂，以使这些数据集适用于未来的精确发现工作。