Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks, of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large specialized databases for animal tracking systems and confirm the utility of our new ape database.

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional "center of mass" tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

动物姿势估计和跟踪（APT）是从一系列视频帧中检测和跟踪动物关键的基本任务。以前与动物有关的数据集专注于动物跟踪或单帧动物姿势估计，而从未在这两个方面上进行。缺乏APT数据集​​阻碍了基于视频的动物姿势估计和跟踪方法的开发和评估，限制了现实世界中的应用，例如了解野生动物保护中的动物行为。为了填补这一空白，我们迈出了第一步，并提出了APT-36K，即第一个用于动物姿势估计和跟踪的大规模基准。具体而言，APT-36K由2,400个视频剪辑组成，并从30种动物物种中收集并过滤，每个视频为15帧，总共产生36,000帧。在手动注释和仔细的双重检查之后，为所有动物实例提供了高质量的关键点和跟踪注释。基于APT-36K，我们在以下三个曲目上基准了几个代表性模型：（1）在内部和域间传输学习设置下，在单个框架上进行监督的动物姿势估计，（2）未见的种间域域内概括测试动物，（3）动物跟踪的动物姿势估计。根据实验结果，我们获得了一些经验见解，并表明APT-36K提供了有价值的动物姿势估计和跟踪基准，为未来的研究提供了新的挑战和机会。该代码和数据集将在https://github.com/pandorgan/apt-36k上公​​开提供。

准确的动物姿势估计是了解动物行为的重要步骤，并且可能有利于许多下游应用，例如野生动物保护。以前的作用仅关注特定动物，同时忽略动物物种的多样性，限制泛化能力。在本文中，我们提出了哺乳动物动物姿势估计的第一个大规模基准的AP-10K，以促进动物姿势估计的研究。 AP-10K由10,015张图像组成，并在分类规模和54种物种之后从23个动物家庭和54种物种，标有标记和检查的高质量Keypoint注释。基于AP-10K，我们在以下三个轨道上基准代表姿态估计模型：（1）监督动物姿势估计的学习，（2）从人类姿势估计到动物姿势估计的跨域转移，和（3） - 看不见的动物的家庭间域概括。实验结果为学习的优越性从精度和泛化能力方面提供了关于从不同的动物物种的学习的优势提供的声音。它开辟了促进动物姿势估计未来研究的新方向。 AP-10K公开提供HTTPS://github.com/alexthebad/ap10k。

瑜伽是全球广受好评的，广泛推荐的健康生活实践。在执行瑜伽时保持正确的姿势至关重要。在这项工作中，我们采用了从人类姿势估计模型中的转移学习来提取整个人体的136个关键点，以训练一个随机的森林分类器，该分类器用于估算瑜伽室。在内部收集的内部收集的瑜伽视频数据库中评估了结果，该数据库是从4个不同的相机角度记录的51个主题。我们提出了一个三步方案，用于通过对1）看不见的帧，2）看不见的受试者进行测试来评估瑜伽分类器的普遍性。我们认为，对于大多数应用程序，对看不见的主题的验证精度和看不见的摄像头是最重要的。我们经验分析了三个公共数据集，转移学习的优势以及目标泄漏的可能性。我们进一步证明，分类精度在很大程度上取决于所采用的交叉验证方法，并且通常会产生误导。为了促进进一步的研究，我们已公开提供关键点数据集和代码。

动物运动跟踪和姿势识别的进步一直是动物行为研究的游戏规则改变者。最近，越来越多的作品比跟踪“更深”，并解决了对动物内部状态（例如情绪和痛苦）的自动认识，目的是改善动物福利，这使得这是对该领域进行系统化的及时时刻。本文对基于计算机的识别情感状态和动物的疼痛的研究进行了全面调查，并涉及面部行为和身体行为分析。我们总结了迄今为止在这个主题中所付出的努力 - 对它们进行分类，从不同的维度进行分类，突出挑战和研究差距，并提供最佳实践建议，以推进该领域以及一些未来的研究方向。

多代理行为建模旨在了解代理之间发生的交互。我们从行为神经科学，Caltech鼠标社交交互（CALMS21）数据集中提供了一个多代理数据集。我们的数据集由社交交互的轨迹数据组成，从标准居民入侵者测定中自由行为小鼠的视频记录。为了帮助加速行为研究，CALMS21数据集提供基准，以评估三种设置中自动行为分类方法的性能：（1）用于培训由单个注释器的所有注释，（2）用于风格转移以进行学习互动在特定有限培训数据的新行为学习的行为定义和（3）的注释差异。 DataSet由600万个未标记的追踪姿势的交互小鼠组成，以及超过100万帧，具有跟踪的姿势和相应的帧级行为注释。我们的数据集的挑战是能够使用标记和未标记的跟踪数据准确地对行为进行分类，以及能够概括新设置。

本文调查了2D全身人类姿势估计的任务，该任务旨在将整个人体（包括身体，脚，脸部和手）局部定位在整个人体上。我们提出了一种称为Zoomnet的单网络方法，以考虑到完整人体的层次结构，并解决不同身体部位的规模变化。我们进一步提出了一个称为Zoomnas的神经体系结构搜索框架，以促进全身姿势估计的准确性和效率。Zoomnas共同搜索模型体系结构和不同子模块之间的连接，并自动为搜索的子模块分配计算复杂性。为了训练和评估Zoomnas，我们介绍了第一个大型2D人类全身数据集，即可可叶全体V1.0，它注释了133个用于野外图像的关键点。广泛的实验证明了Zoomnas的有效性和可可叶v1.0的重要性。

海洋正在经历前所未有的快速变化，在负责任管理所需的时空尺度上，视觉监测海洋生物群是一项艰巨的任务。由于研究界寻求基准，因此所需的数据收集的数量和速率迅速超过了我们处理和分析它们的能力。机器学习的最新进展可以对视觉数据进行快速，复杂的分析，但由于缺乏数据标准化，格式不足以及对大型标签数据集的需求，在海洋中取得了有限的成功。为了满足这一需求，我们构建了Fathomnet，这是一个开源图像数据库，该数据库标准化和汇总了经过精心策划的标记数据。 Fathomnet已被海洋动物，水下设备，碎片和其他概念的现有标志性和非偶像图像所播种，并允许分布式数据源的未来贡献。我们展示了如何使用Fathomnet数据在其他机构视频上训练和部署模型，以减少注释工作，并在与机器人车辆集成时启用自动跟踪水下概念。随着Fathomnet继续增长并结合了社区的更多标记数据，我们可以加速视觉数据以实现健康且可持续的全球海洋。

自发运动评估可以预测高风险婴儿的长期发育障碍。为了开发用于后续疾病的自动预测的算法，需要通过婴儿姿势估计的段和关节的高精度定位。训练了四种类型的卷积神经网络，并在新颖的婴儿姿势数据集上进行培训，并从临床国际社会中涵盖了1224个视频的大变化。将网络的本地化性能评估为估计的关键点位置和人类专家注释之间的偏差。还评估了计算效率，以确定神经网络在临床实践中的可行性。表现最佳的神经网络对人类专家注释的帧间扩散具有类似的本地化误差，同时仍然有效地运行。总体而言，我们的研究结果表明，婴儿自发运动的姿势估计有巨大的潜力，支持研究潜冲在早期检测儿童发育疾病的潜在脑损伤的发育障碍，这些脑卒中与人为水平绩效的录像量。

随着牛奶厂的畜群大小继续增加，奶牛的自动健康监测是兴趣的。通过分析奶牛的步态，通常检测到乳制品奶牛中普遍存在的健康障碍。可以使用姿势估计模型在视频中追踪牛的步态，因为模型学会自动本地化图像和视频的解剖标志。大多数动物姿势估计模型是静态的，也就是说，视频是通过帧进行处理的帧，并且不使用任何时间信息。在这项工作中，用于动物姿态估计的静态深度学习模型扩展到包括来自过去框架的信息的时间模型。我们比较了静态和时间姿势估计模型的性能。这些数据由来自通过室外通道的30个不同乳制品牛的视频（30 fps）提取的459个三个连续帧。随着农场环境易于闭塞，我们通过向视频添加人工闭塞来测试静态和时间模型的稳健性。实验表明，在非闭塞数据中，静态和时间方法都达到了正确关键点的百分比（PCKH @ 0.2）99％。在遮挡数据上，我们的时间方法优于静态，高达32.9％，表明使用时间数据有利于易于闭塞的环境中的姿势估计，例如乳制品农场。通过在包含未知奶牛的数据（培训集中不存在的奶牛）上测试了时间模型的泛化能力。结果表明，普通PCKH@0.2在已知的奶牛上为93.8％，在未知的奶牛上为87.6％，表明该模型能够概括为新奶牛，并且它们可以很容易地微调到新牛群。最后，我们展示了更难的任务，例如遮挡和未知的奶牛，更深层次的建筑更有益。

现实世界的行为通常是由多种代理之间复杂的相互作用来塑造的。为了可靠地研究多代理行为，无监督和自我监督的学习的进步使从轨迹数据中学到了各种不同的行为表示。迄今为止，还没有一组统一的基准测试，可以在广泛的行为分析设置中进行定量和系统地比较方法。我们的目的是通过引入来自现实世界行为神经科学实验的大规模，多代理轨迹数据集来解决这一问题，该数据集涵盖了一系列行为分析任务。我们的数据集由来自通用模型生物的轨迹数据组成，其中有960万帧的小鼠数据和440万帧的飞行数据，在各种实验环境中，例如不同的菌株，相互作用的长度和光遗传学刺激。框架的子集还包括专家注销的行为标签。我们数据集的改进对应于跨多种生物的行为表示，并能够捕获常见行为分析任务的差异。

由于几个因素之间的微妙权衡：参与者的隐私，生态有效性，数据保真度和后勤开销，记录野外未脚本人类互动的动态是具有挑战性的。为了解决这些问题，在社区精神上为社区的“数据集”之后，我们提出了会议生活实验室（Conflab）：一个新的概念，用于多模式多模式数据收集，野生野外社交对话。对于此处描述的Conflab的首次实例化，我们在一次大型国际会议上组织了现实生活中的专业网络活动。该数据集涉及48个会议参与者，捕捉了地位，熟人和网络动机的各种组合。我们的捕获设置改善了先前野外数据集的数据保真度，同时保留隐私敏感性：从非侵入性的架空视图中获得8个视频（1920x1080，60 fps），并具有定制的可穿戴传感器，并带有车载记录（完整9） - 轴IMU），具有隐私性的低频音频（1250 Hz）和基于蓝牙的接近度。此外，我们开发了用于采集时分布式硬件同步的自定义解决方案，并以高采样速率对身体关键点和动作进行了及时的连续注释。我们的基准测试展示了与野外隐私保护社交数据分析有关的一些开放研究任务：从高架摄像头视图，基于骨架的No-Audio扬声器检测和F-Formation检测中的关键点检测。

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current nonensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.

Studying facial expressions is a notoriously difficult endeavor. Recent advances in the field of affective computing have yielded impressive progress in automatically detecting facial expressions from pictures and videos. However, much of this work has yet to be widely disseminated in social science domains such as psychology. Current state of the art models require considerable domain expertise that is not traditionally incorporated into social science training programs. Furthermore, there is a notable absence of user-friendly and open-source software that provides a comprehensive set of tools and functions that support facial expression research. In this paper, we introduce Py-Feat, an open-source Python toolbox that provides support for detecting, preprocessing, analyzing, and visualizing facial expression data. Py-Feat makes it easy for domain experts to disseminate and benchmark computer vision models and also for end users to quickly process, analyze, and visualize face expression data. We hope this platform will facilitate increased use of facial expression data in human behavior research.

我们提出了一种学习来自未标识的行为视频的代理的姿势和结构的方法。从观察开始，表现代理通常是行为视频中的主要运动来源，我们的方法使用具有几何瓶颈的编码器 - 解码器架构来重建视频帧之间的差异。只要仅关注运动区域，我们的方法直接在输入视频上工作，而无需手动注释，例如关键点或边界框。关于各种代理类型（鼠标，飞，人，水母和树木）的实验展示了我们的方法的一般性，并揭示了我们发现的关键点代表着语义有意义的身体部位，这在关键点回归上实现了最先进的性能在自我监督的方法中。此外，我们发现的关键点可实现可比的性能，以对下游任务的监督关键点，例如行为分类，表明我们的方法可以大大降低模型培训VIS-VIS监督方法的成本。

3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-thewild". However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-ofthe art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.* This work was performed while J. Romero and F. Bogo were with the MPI-IS 2 ; P. V. Gehler with the BCCN 1 and MPI-IS 2 .

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-

In this paper, we are interested in the human pose estimation problem with a focus on learning reliable highresolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process.We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutliresolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich highresolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. The code and models have been publicly available at https://github.com/leoxiaobin/ deep-high-resolution-net.pytorch.