最近，数据增强已成为视觉识别任务的现代培训食谱的重要组成部分。但是，尽管有效性，但很少探索视频识别的数据增强。很少有用于视频识别的现有增强食谱通过将相同的操作应用于整个视频框架来天真地扩展图像增强方法。我们的主要思想是，每帧的增强操作的大小都需要随着时间的推移而更改，以捕获现实世界视频的时间变化。在训练过程中，应使用更少的额外超参数来尽可能多地生成这些变化。通过这种动机，我们提出了一个简单而有效的视频数据增强框架Dynaaugment。每个帧上增强操作的大小通过有效的机制，傅立叶采样更改，该采样将各种，平滑和现实的时间变化参数化。 Dynaaugment还包括一个适用于视频的扩展搜索空间，用于自动数据增强方法。 Dynaaugment在实验上表明，从各种视频模型的静态增强中可以改善其他性能室。具体而言，我们在各种视频数据集和任务上显示了Dynaaugment的有效性：大规模视频识别（Kinetics-400和Sothings-Something-v2），小规模视频识别（UCF-101和HMDB-51），精细元素视频识别（潜水48和FINEGYM），早餐的视频动作细分，Thumos'14上的视频动作本地化以及MOT17DET上的视频对象检测。 Dynaaugment还使视频模型能够学习更广泛的表示形式，以改善损坏视频的模型鲁棒性。

Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality of generated views. However, most existing methods lack a mechanism to prevent representation learning from bias towards static information in the video. In this paper, we propose frequency augmentation (FreqAug), a spatio-temporal data augmentation method in the frequency domain for video representation learning. FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks. Specifically, FreqAug pushes the model to focus more on dynamic features rather than static features in the video via dropping spatial or temporal low-frequency components. To verify the generality of the proposed method, we experiment with FreqAug on multiple self-supervised learning frameworks along with standard augmentations. Transferring the improved representation to five video action recognition and two temporal action localization downstream tasks shows consistent improvements over baselines.

大多数现有的深神经网络都是静态的，这意味着它们只能以固定的复杂性推断。但资源预算可以大幅度不同。即使在一个设备上，实惠预算也可以用不同的场景改变，并且对每个所需预算的反复培训网络是非常昂贵的。因此，在这项工作中，我们提出了一种称为Mutualnet的一般方法，以训练可以以各种资源约束运行的单个网络。我们的方法列举了具有各种网络宽度和输入分辨率的模型配置队列。这种相互学习方案不仅允许模型以不同的宽度分辨率配置运行，而且还可以在这些配置之间传输独特的知识，帮助模型来学习更强大的表示。 Mutualnet是一般的培训方法，可以应用于各种网络结构（例如，2D网络：MobileNets，Reset，3D网络：速度，X3D）和各种任务（例如，图像分类，对象检测，分段和动作识别），并证明了实现各种数据集的一致性改进。由于我们只培训了这一模型，它对独立培训多种型号而言，它也大大降低了培训成本。令人惊讶的是，如果动态资源约束不是一个问题，则可以使用Mutualnet来显着提高单个网络的性能。总之，Mutualnet是静态和自适应，2D和3D网络的统一方法。代码和预先训练的模型可用于\ url {https://github.com/tayang1122/mutualnet}。

Although action recognition systems can achieve top performance when evaluated on in-distribution test points, they are vulnerable to unanticipated distribution shifts in test data. However, test-time adaptation of video action recognition models against common distribution shifts has so far not been demonstrated. We propose to address this problem with an approach tailored to spatio-temporal models that is capable of adaptation on a single video sample at a step. It consists in a feature distribution alignment technique that aligns online estimates of test set statistics towards the training statistics. We further enforce prediction consistency over temporally augmented views of the same test video sample. Evaluations on three benchmark action recognition datasets show that our proposed technique is architecture-agnostic and able to significantly boost the performance on both, the state of the art convolutional architecture TANet and the Video Swin Transformer. Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts. Code will be available at \url{https://github.com/wlin-at/ViTTA}.

Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally linked series of data. Recently, it has been shown that involving the time dimension when designing augmentations can be superior to its spatial-only variants for video action recognition. In this paper, we propose several novel enhancements to these techniques to strengthen the relationship between the spatial and temporal domains and achieve a deeper level of perturbations. The video action recognition results of our techniques outperform their respective variants in Top-1 and Top-5 settings on the UCF-101 and the HMDB-51 datasets.

近年来，计算机视觉社区中最受欢迎的技术之一就是深度学习技术。作为一种数据驱动的技术，深层模型需要大量准确标记的培训数据，这在许多现实世界中通常是无法访问的。数据空间解决方案是数据增强（DA），可以人为地从原始样本中生成新图像。图像增强策略可能因数据集而有所不同，因为不同的数据类型可能需要不同的增强以促进模型培训。但是，DA策略的设计主要由具有领域知识的人类专家决定，这被认为是高度主观和错误的。为了减轻此类问题，一个新颖的方向是使用自动数据增强（AUTODA）技术自动从给定数据集中学习图像增强策略。 Autoda模型的目的是找到可以最大化模型性能提高的最佳DA策略。这项调查从图像分类的角度讨论了Autoda技术出现的根本原因。我们确定标准自动赛车模型的三个关键组件：搜索空间，搜索算法和评估功能。根据他们的架构，我们提供了现有图像AUTODA方法的系统分类法。本文介绍了Autoda领域的主要作品，讨论了他们的利弊，并提出了一些潜在的方向以进行未来的改进。

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AUGMIX, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AUGMIX significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.

图像预训练，当前用于广泛视觉任务的当前事实范式在视频识别领域中通常不太受青睐。相比之下，一种共同的策略是直接从头开始使用时空卷积神经网络（CNN）训练。尽管如此，有趣的是，通过仔细研究这些从划痕学到的CNN，我们注意到存在某些3D内核比其他人具有更强的外观建模能力，可以说表明外观信息在学习中已经很好地散布了。受到这一观察的启发，我们假设有效利用图像预训练的关键在于学习空间和时间特征的分解，并将图像预训练作为初始化3D内核之前的外观。此外，我们提出了空间可分离（STS）卷积，该卷积将特征通道明确将特征通道分为空间和时间基团，以进一步使时空特征更彻底地分解3D CNN。我们的实验表明，简单地用ST替换3D卷积可以显着改善3D CNN的范围，而无需增加参数和计算动力学400和一些v2的计算。此外，这条新的培训管道始终以显着加速的视频识别取得更好的结果。例如，在强大的256- epecoch 128-GPU基线上，我们在Kinetics-400上获得了 +0.6％的慢速1，同时仅以40个GPU进行微调，而对50个时代进行了微调。代码和型号可在https://github.com/ucsc-vlaa/image-pretraining-for-video上找到。

Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly demand learning the intensive representation of long videos. In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos. Specifically, we introduce a simple but effective video encoder that considers both spatial and temporal context by combining convolution and transformer. Inspired by the recent massive progress in self-supervised learning, we propose a new sequence contrast loss (SCL) applied to two related views obtained by expanding a series of spatio-temporal data in two versions. One is the self-supervised version that optimizes embedding space by minimizing KL-divergence between sequence similarity of two augmented views and prior Gaussian distribution of timestamp distance. The other is the weakly-supervised version that builds more sample pairs among videos using video-level labels by dynamic time wrapping (DTW). Experiments on FineGym, PennAction, and Pouring datasets show that our method outperforms previous state-of-the-art by a large margin for downstream fine-grained action classification and even faster inference. Surprisingly, although without training on paired videos like in previous works, our self-supervised version also shows outstanding performance in video alignment and fine-grained frame retrieval tasks.

尽管视频自我监督的学习模型最近取得了成功，但关于它们的概括能力仍然有很多了解。在本文中，我们研究了敏感的视频自我监督学习对当前常规基准的方式以及方法是否超出规范评估设置的概括。我们在敏感性的四个不同因素上做到这一点：域，样本，动作和任务。我们的研究包括7个视频数据集，9种自学方法和6种视频理解任务的500多个实验，揭示了视频自我监督学习中的当前基准测试不是沿这些敏感性因素的概括指标。此外，我们发现自我监督的方法在香草的监督前训练后落后，尤其是当域移动较大并且可用下游样品的量很低时。从我们的分析中，我们将严重的基准测试（实验的一个子集）提炼出来，并讨论其对评估现有和未来自我监督视频学习方法获得的表示的普遍性的意义。

Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout remove informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it leads to information loss and inefficiency during training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CI-FAR and ImageNet classification tasks, as well as on the Im-ageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances. Source code and pretrained models are available at https://github.com/clovaai/CutMix-PyTorch.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

有效地对视频中的空间信息进行建模对于动作识别至关重要。为了实现这一目标，最先进的方法通常采用卷积操作员和密集的相互作用模块，例如非本地块。但是，这些方法无法准确地符合视频中的各种事件。一方面，采用的卷积是有固定尺度的，因此在各种尺度的事件中挣扎。另一方面，密集的相互作用建模范式仅在动作 - 欧元零件时实现次优性能，给最终预测带来了其他噪音。在本文中，我们提出了一个统一的动作识别框架，以通过引入以下设计来研究视频内容的动态性质。首先，在提取本地提示时，我们会生成动态尺度的时空内核，以适应各种事件。其次，为了将这些线索准确地汇总为全局视频表示形式，我们建议仅通过变压器在一些选定的前景对象之间进行交互，从而产生稀疏的范式。我们将提出的框架称为事件自适应网络（EAN），因为这两个关键设计都适应输入视频内容。为了利用本地细分市场内的短期运动，我们提出了一种新颖有效的潜在运动代码（LMC）模块，进一步改善了框架的性能。在几个大规模视频数据集上进行了广泛的实验，例如，某种东西，动力学和潜水48，验证了我们的模型是否在低拖鞋上实现了最先进或竞争性的表演。代码可在：https：//github.com/tianyuan168326/ean-pytorch中找到。

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。

我们向您展示一次（YOCO）进行数据增强。 Yoco将一张图像切成两片，并在每件零件中单独执行数据增强。应用YOCO改善了每个样品的增强的多样性，并鼓励神经网络从部分信息中识别对象。 Yoco享受无参数，轻松使用的属性，并免费提供几乎所有的增强功能。进行了彻底的实验以评估其有效性。我们首先证明Yoco可以无缝地应用于不同的数据增强，神经网络体系结构，并在CIFAR和Imagenet分类任务上带来性能提高，有时会超过传统的图像级增强。此外，我们显示了Yoco益处对比的预培训，以更强大的表示，可以更好地转移到多个下游任务。最后，我们研究了Yoco的许多变体，并经验分析了各个设置的性能。代码可在GitHub上找到。

Recent work has shown that data augmentation has the potential to significantly improve the generalization of deep learning models. Recently, automated augmentation strategies have led to state-of-the-art results in image classification and object detection. While these strategies were optimized for improving validation accuracy, they also led to state-of-the-art results in semi-supervised learning and improved robustness to common corruptions of images. An obstacle to a large-scale adoption of these methods is a separate search phase which increases the training complexity and may substantially increase the computational cost. Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size. Automated augmentation policies are often found by training small models on small datasets and subsequently applied to train larger models. In this work, we remove both of these obstacles. RandAugment has a significantly reduced search space which allows it to be trained on the target task with no need for a separate proxy task. Furthermore, due to the parameterization, the regularization strength may be tailored to different model and dataset sizes. RandAugment can be used uniformly across different tasks and datasets and works out of the box, matching or surpassing all previous automated augmentation approaches on CIFAR-10/100, SVHN, and ImageNet. On the ImageNet dataset we achieve 85.0% accuracy, a 0.6% increase over the previous state-of-the-art and 1.0% increase over baseline augmentation. On object detection, RandAugment leads to 1.0-1.3% improvement over baseline augmentation, and is within 0.3% mAP of AutoAugment on COCO. Finally, due to its interpretable hyperparameter, RandAugment may be used to investigate the role of data augmentation with varying model and dataset size. Code is available online. 1 * Authors contributed equally.1 github.com/tensorflow/tpu/tree/master/models/ official/efficientnet

这项工作提出了一个名为TEG的自我监督的学习框架，探讨学习视频表示中的时间粒度。在TEG中，我们从视频中抽出一个长剪辑，以及在长夹内部的短夹。然后我们提取密集的时间嵌入品。培训目标由两部分组成：一个细粒度的时间学习目的，以最大化短夹和长剪辑中的相应时间嵌入之间的相似性，以及持续的时间学习目标，以将两个剪辑的全局嵌入在一起。我们的研究揭示了时间粒度与三个主要发现的影响。 1）不同的视频任务可能需要不同时间粒度的特征。 2）有趣的是，广泛认为需要时间感知的一些任务实际上可以通过时间持久的功能来解决。 3）TEG的灵活性对8个视频基准测试产生最先进的结果，在大多数情况下优于监督预训练。

时间活动检测旨在预测每帧的活动类，与活动分类中所做的视频级预测相比，如活动分类（即，活动识别）对比。由于检测所需的昂贵的帧级注释，检测数据集的比例是有限的。因此，通常，以前的工作对时间活动检测度假难进行微调，在大规模分类数据集（例如动力学-400）上进行微调的分类模型。然而，由于预先预测和下游微调任务之间的视差，这种预磨损的模型并不理想的是下游检测性能。这项工作提出了一种用于检测利用分类标签的新型自我监督预测方法，通过引入帧级伪标签，多动作帧和动作段来减轻这种差异。我们表明，使用拟议的自我监督检测任务预先磨削的模型始终是在多个具有挑战性的活动检测基准上的现有工作，包括Charades和Multithumos。我们广泛的消融进一步提供了关于何时以及如何使用所提出的活动检测模型的见解。代码和模型将在线发布。

在本文中，我们将多尺度视觉变压器（MVIT）作为图像和视频分类的统一架构，以及对象检测。我们提出了一种改进的MVIT版本，它包含分解的相对位置嵌入和残余汇集连接。我们以五种尺寸实例化此架构，并评估Imagenet分类，COCO检测和动力学视频识别，在此优先效果。我们进一步比较了MVITS的汇集注意力来窗口注意力机制，其中它在准确性/计算中优于后者。如果没有钟声，MVIT在3个域中具有最先进的性能：ImageNet分类的准确性为88.8％，Coco对象检测的56.1盒AP和动力学-400视频分类的86.1％。代码和模型将公开可用。

We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial. We carefully design data augmentations involving spatial and temporal cues. Concretely, we propose a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. We also propose a sampling-based temporal augmentation method to avoid overly enforcing invariance on clips that are distant in time. On Kinetics-600, a linear classifier trained on the representations learned by CVRL achieves 70.4% top-1 accuracy with a 3D-ResNet-50 (R3D-50) backbone, outperforming ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8% using the same inflated R3D-50. The performance of CVRL can be further improved to 72.9% with a larger R3D-152 (2× filters) backbone, significantly closing the gap between unsupervised and supervised video representation learning. Our code and models will be available at https://github.com/tensorflow/models/tree/master/official/.