One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. In this paper, we propose Rendezvous in Time (RiT) - a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as (instrument, verb). Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition.

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of <instrument, verb, target> combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

未来的活动预期是在Egocentric视觉中具有挑战性问题。作为标准的未来活动预期范式，递归序列预测遭受错误的累积。为了解决这个问题，我们提出了一个简单有效的自我监管的学习框架，旨在使中间表现为连续调节中间代表性，以产生表示（a）与先前观察到的对比的当前时间戳框架中的新颖信息内容和（b）反映其与先前观察到的帧的相关性。前者通过最小化对比损失来实现，并且后者可以通过动态重量机制来实现在观察到的内容中的信息帧中，具有当前帧的特征与观察到的帧之间的相似性比较。通过多任务学习可以进一步增强学习的最终视频表示，该多任务学习在目标活动标签上执行联合特征学习和自动检测到的动作和对象类令牌。在大多数自我传统视频数据集和两个第三人称视频数据集中，SRL在大多数情况下急剧表现出现有的现有最先进。通过实验性事实，还可以准确识别支持活动语义的行动和对象概念的实验性。

基于文本的视频细分旨在通过用文本查询指定演员及其表演动作来细分视频序列中的演员。由于\ emph {emph {语义不对称}的问题，以前的方法无法根据演员及其动作以细粒度的方式将视频内容与文本查询对齐。 \ emph {语义不对称}意味着在多模式融合过程中包含不同量的语义信息。为了减轻这个问题，我们提出了一个新颖的演员和动作模块化网络，该网络将演员及其动作分别定位在两个单独的模块中。具体来说，我们首先从视频和文本查询中学习与参与者相关的内容，然后以对称方式匹配它们以定位目标管。目标管包含所需的参与者和动作，然后将其送入完全卷积的网络，以预测演员的分割掩模。我们的方法还建立了对象的关联，使其与所提出的时间建议聚合机制交叉多个框架。这使我们的方法能够有效地细分视频并保持预测的时间一致性。整个模型允许联合学习参与者的匹配和细分，并在A2D句子和J-HMDB句子数据集上实现单帧细分和完整视频细分的最新性能。

最近，视频变压器在视频理解方面取得了巨大成功，超过了CNN性能;然而，现有的视频变换器模型不会明确地模拟对象，尽管对象对于识别操作至关重要。在这项工作中，我们呈现对象区域视频变换器（Orvit），一个\ emph {对象为中心}方法，它与直接包含对象表示的块扩展视频变压器图层。关键的想法是从早期层开始融合以对象形式的表示，并将它们传播到变压器层中，从而影响整个网络的时空表示。我们的orvit块由两个对象级流组成：外观和动态。在外观流中，“对象区域关注”模块在修补程序上应用自我关注和\ emph {对象区域}。以这种方式，Visual对象区域与统一修补程序令牌交互，并通过上下文化对象信息来丰富它们。我们通过单独的“对象 - 动态模块”进一步模型对象动态，捕获轨迹交互，并显示如何集成两个流。我们在四个任务和五个数据集中评估我们的模型：在某事物中的某些问题和几次射击动作识别，以及在AVA上的某些时空动作检测，以及在某种东西上的标准动作识别 - 某种东西 - 东西，潜水48和EPIC-Kitchen100。我们在考虑的所有任务和数据集中展示了强大的性能改进，展示了将对象表示的模型的值集成到变压器体系结构中。对于代码和预用模型，请访问项目页面\ url {https://roeiherz.github.io/orvit/}

Most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Visual Action recognition (PSI-AVA) Dataset. PSI-AVA includes annotations for both long-term (Phase and Step recognition) and short-term reasoning (Instrument detection and novel Atomic Action recognition) in robot-assisted radical prostatectomy videos. Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding. TAPIR leverages our dataset's multi-level annotations as it benefits from the learned representation on the instrument detection task to improve its classification capacity. Our experimental results in both PSI-AVA and other publicly available databases demonstrate the adequacy of our framework to spur future research on holistic surgical scene understanding.

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

自动外科阶段识别在机器人辅助手术中起着重要作用。现有方法忽略了一个关键问题，即外科阶段应该通过学习段级语义来分类，而不是仅仅依赖于框架明智的信息。在本文中，我们提出了一种段 - 细分分层一致性网络（SAHC），用于来自视频的外科阶段识别。关键的想法是提取分层高级语义 - 一致的段，并使用它们来优化由暧昧帧引起的错误预测。为实现它，我们设计一个时间分层网络以生成分层高级段。然后，我们引入分层段帧注意力（SFA）模块，以捕获低级帧和高级段之间的关系。通过通过一致性损耗来规则地规范帧及其对应段的预测，网络可以生成语义 - 一致的段，然后纠正由模糊的低级帧引起的错误分类预测。我们在两个公共外科视频数据集上验证SAHC，即M2CAI16挑战数据集和CholeC80数据集。实验结果表明，我们的方法优于以前的最先进的余量，显着达到M2Cai16的4.1％。代码将在验收时在Github发布。

可穿戴摄像机可以从用户的角度获取图像和视频。可以处理这些数据以了解人类的行为。尽管人类的行为分析已在第三人称视野中进行了彻底的研究，但仍在以自我为中心的环境中，尤其是在工业场景中进行了研究。为了鼓励在该领域的研究，我们介绍了Meccano，这是一个以自我为中心视频的多式模式数据集来研究类似工业的环境中的人类行为理解。多模式的特征是凝视信号，深度图和RGB视频同时使用自定义耳机获得。该数据集已在从第一人称视角的人类行为理解的背景下明确标记为基本任务，例如识别和预测人类对象的相互作用。使用MECCANO数据集，我们探索了五个不同的任务，包括1）动作识别，2）活动对象检测和识别，3）以自我为中心的人类对象互动检测，4）动作预期和5）下一步活动对象检测。我们提出了一个旨在研究人类行为的基准，该基准在被考虑的类似工业的情况下，表明所研究的任务和所考虑的方案对于最先进的算法具有挑战性。为了支持该领域的研究，我们在https://iplab.dmi.unict.it/meccano/上公开发布数据集。

设计可以成功部署在日常生活环境中的活动检测系统需要构成现实情况典型挑战的数据集。在本文中，我们介绍了一个新的未修剪日常生存数据集，该数据集具有几个现实世界中的挑战：Toyota Smarthome Untrimmed（TSU）。 TSU包含以自发方式进行的各种活动。数据集包含密集的注释，包括基本的，复合活动和涉及与对象相互作用的活动。我们提供了对数据集所需的现实世界挑战的分析，突出了检测算法的开放问题。我们表明，当前的最新方法无法在TSU数据集上实现令人满意的性能。因此，我们提出了一种新的基线方法，以应对数据集提供的新挑战。此方法利用一种模态（即视线流）生成注意力权重，以指导另一种模态（即RGB）以更好地检测活动边界。这对于检测以高时间差异为特征的活动特别有益。我们表明，我们建议在TSU和另一个受欢迎的挑战数据集Charades上优于最先进方法的方法。

Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining annotations of scene structure for videos requires a significant amount of effort to gather and annotate, making these methods expensive to train. In contrast, synthetic datasets generated by graphics engines provide powerful alternatives for generating scene-level annotations across multiple tasks. In this work, we propose an approach to leverage synthetic scene data for improving video understanding. We present a multi-task prompt learning approach for video transformers, where a shared video transformer backbone is enhanced by a small set of specialized parameters for each task. Specifically, we add a set of ``task prompts'', each corresponding to a different task, and let each prompt predict task-related annotations. This design allows the model to capture information shared among synthetic scene tasks as well as information shared between synthetic scene tasks and a real video downstream task throughout the entire network. We refer to this approach as ``Promptonomy'', since the prompts model a task-related structure. We propose the PromptonomyViT model (PViT), a video transformer that incorporates various types of scene-level information from synthetic data using the ``Promptonomy'' approach. PViT shows strong performance improvements on multiple video understanding tasks and datasets.

计算机视觉任务可以从估计突出物区域和这些对象区域之间的相互作用中受益。识别对象区域涉及利用预借鉴模型来执行对象检测，对象分割和/或对象姿势估计。但是，由于以下原因，在实践中不可行：1）预用模型的训练数据集的对象类别可能不会涵盖一般计算机视觉任务的所有对象类别，2）佩戴型模型训练数据集之间的域间隙并且目标任务的数据集可能会影响性能，3）预磨模模型中存在的偏差和方差可能泄漏到导致无意中偏置的目标模型的目标任务中。为了克服这些缺点，我们建议利用一系列视频帧捕获一组公共对象和它们之间的相互作用的公共基本原理，因此视频帧特征之间的共分割的概念可以用自动的能力装配模型专注于突出区域，以最终的方式提高潜在的任务的性能。在这方面，我们提出了一种称为“共分割激活模块”（COSAM）的通用模块，其可以被插入任何CNN，以促进基于CNN的任何CNN的概念在一系列视频帧特征中的关注。我们在三个基于视频的任务中展示Cosam的应用即1）基于视频的人Re-ID，2）视频字幕分类，并证明COSAM能够在视频帧中捕获突出区域，从而引导对于显着的性能改进以及可解释的关注图。

手术视频中的活动识别是开发下一代设备和工作流程监测系统的关键研究领域。由于手术是具有高度变化长度的较长过程，因此用于手术视频的深度学习模型通常包括使用主链和时间序列模型的两阶段设置。在本文中，我们研究了许多最新的骨干和时间模型，以找到为手术活动识别提供最强性能的体系结构。我们首先在大规模活动识别数据集上进行模型性能，该数据集包含在多个临床手术室中捕获的800多个手术视频。我们进一步评估了两个较小的公共数据集（Cholec80和Cataract-101数据集）上的模型，分别包含80个视频和101个视频。我们从经验上发现，Swin-Transformer+BigRU时间模型在两个数据集上都产生了强劲的性能。最后，我们通过对新医院进行微调模型来研究模型对新领域的适应性，并试验最近无监督的域适应方法。

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.

第一人称行动认可是视频理解中有挑战性的任务。由于强烈的自我运动和有限的视野，第一人称视频中的许多背景或嘈杂的帧可以在其学习过程中分散一个动作识别模型。为了编码更多的辨别特征，模型需要能够专注于视频的最相关的动作识别部分。以前的作品通过应用时间关注但未能考虑完整视频的全局背景来解决此问题，这对于确定相对重要的部分至关重要。在这项工作中，我们提出了一种简单而有效的堆叠的临时注意力模块（STAM），以基于跨越剪辑的全球知识来计算时间注意力，以强调最辨别的特征。我们通过堆叠多个自我注意层来实现这一目标。而不是天真的堆叠，这是实验证明是无效的，我们仔细地设计了每个自我关注层的输入，以便在产生时间注意力期间考虑视频的本地和全局背景。实验表明，我们提出的STAM可以基于大多数现有底座的顶部构建，并提高各个数据集中的性能。

在这项工作中，我们呈现SEQFormer，这是一个令人沮丧的视频实例分段模型。 SEQFormer遵循Vision变换器的原理，该方法模型视频帧之间的实例关系。然而，我们观察到一个独立的实例查询足以捕获视频中的时间序列，但应该独立地使用每个帧进行注意力机制。为此，SEQFormer在每个帧中定位一个实例，并聚合时间信息以学习视频级实例的强大表示，其用于动态地预测每个帧上的掩模序列。实例跟踪自然地实现而不进行跟踪分支或后处理。在YouTube-VIS数据集上，SEQFormer使用Reset-50个骨干和49.0 AP实现47.4个AP，其中Reset-101骨干，没有响铃和吹口哨。此类成果分别显着超过了以前的最先进的性能4.6和4.4。此外，与最近提出的Swin变压器集成，SEQFormer可以实现59.3的高得多。我们希望SEQFormer可能是一个强大的基线，促进了视频实例分段中的未来研究，同时使用更强大，准确，整洁的模型来实现该字段。代码和预先训练的型号在https://github.com/wjf5203/seqformer上公开使用。

Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition.

具有注释的缺乏大规模的真实数据集使转移学习视频活动的必要性。我们的目标是为少数行动分类开发几次拍摄转移学习的有效方法。我们利用独立培训的本地视觉提示来学习可以从源域传输的表示，该源域只能使用少数示例来从源域传送到不同的目标域。我们使用的视觉提示包括对象 - 对象交互，手掌和地区内的动作，这些地区是手工位置的函数。我们采用了一个基于元学习的框架，以提取部署的视觉提示的独特和域不变组件。这使得能够在使用不同的场景和动作配置捕获的公共数据集中传输动作分类模型。我们呈现了我们转让学习方法的比较结果，并报告了阶级阶级和数据间数据间际传输的最先进的行动分类方法。

Modeling the visual changes that an action brings to a scene is critical for video understanding. Currently, CNNs process one local neighbourhood at a time, thus contextual relationships over longer ranges, while still learnable, are indirect. We present TROI, a plug-and-play module for CNNs to reason between mid-level feature representations that are otherwise separated in space and time. The module relates localized visual entities such as hands and interacting objects and transforms their corresponding regions of interest directly in the feature maps of convolutional layers. With TROI, we achieve state-of-the-art action recognition results on the large-scale datasets Something-Something-V2 and EPIC-Kitchens-100.

Anticipating future actions based on video observations is an important task in video understanding, which would be useful for some precautionary systems that require response time to react before an event occurs. Since the input in action anticipation is only pre-action frames, models do not have enough information about the target action; moreover, similar pre-action frames may lead to different futures. Consequently, any solution using existing action recognition models can only be suboptimal. Recently, researchers have proposed using a longer video context to remedy the insufficient information in pre-action intervals, as well as the self-attention to query past relevant moments to address the anticipation problem. However, the indirect use of video input features as the query might be inefficient, as it only serves as the proxy to the anticipation goal. To this end, we propose an inductive attention model, which transparently uses prior prediction as the query to derive the anticipation result by induction from past experience. Our method naturally considers the uncertainty of multiple futures via the many-to-many association. On the large-scale egocentric video datasets, our model not only shows consistently better performance than state of the art using the same backbone, and is competitive to the methods that employ a stronger backbone, but also superior efficiency in less model parameters.