In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that can localize the object that sounds in an image, given the audio signal. We achieve both these objectives by training from unlabelled video using only audio-visual correspondence (AVC) as the objective function. This is a form of crossmodal self-supervision from video. To this end, we design new network architectures that can be trained for cross-modal retrieval and localizing the sound source in an image, by using the AVC task. We make the following contributions: (i) show that audio and visual embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and between-mode retrieval; (ii) explore various architectures for the AVC task, including those for the visual stream that ingest a single image, or multiple images, or a single image and multi-frame optical flow; (iii) show that the semantic object that sounds within an image can be localized (using only the sound, no motion or flow information); and (iv) give a cautionary tale on how to avoid undesirable shortcuts in the data preparation.

We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself -the correspondence between the visual and the audio streams, and we introduce a novel "Audio-Visual Correspondence" learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks.

There is a natural correlation between the visual and auditive elements of a video. In this work we leverage this connection to learn general and effective models for both audio and video analysis from self-supervised temporal synchronization. We demonstrate that a calibrated curriculum learning scheme, a careful choice of negative examples, and the use of a contrastive loss are critical ingredients to obtain powerful multi-sensory representations from models optimized to discern temporal synchronization of audio-video pairs. Without further finetuning, the resulting audio features achieve performance superior or comparable to the state-of-the-art on established audio classification benchmarks (DCASE2014 and ESC-50). At the same time, our visual subnet provides a very effective initialization to improve the accuracy of video-based action recognition models: compared to learning from scratch, our self-supervised pretraining yields a remarkable gain of +19.9% in action recognition accuracy on UCF101 and a boost of +17.7% on HMDB51.

我们在没有监督的情况下解决了学习对象探测器的问题。与弱监督的对象检测不同，我们不假设图像级类标签。取而代之的是，我们使用音频组件来“教”对象检测器，从视听数据中提取监督信号。尽管此问题与声音源本地化有关，但它更难，因为检测器必须按类型对对象进行分类，列举对象的每个实例，并且即使对象保持沉默，也可以这样做。我们通过首先设计一个自制的框架来解决这个问题，该框架具有一个对比目标，该目标共同学会了分类和本地化对象。然后，在不使用任何监督的情况下，我们只需使用这些自我监督的标签和盒子来训练基于图像的对象检测器。因此，对于对象检测和声音源定位的任务，我们优于先前的无监督和弱监督的检测器。我们还表明，我们可以将该探测器与每个伪级标签的标签保持一致，并展示我们的方法如何学习检测超出仪器（例如飞机和猫）的通用对象。

在本文中，我们考虑了视听同步的问题应用于视频`in-wild'（即，超越语音的一般类）。作为一项新任务，我们识别并策划具有高视听相关性的测试集，即VGG-SOCK SYNC。我们比较了一些专门设计的基于变压器的架构变体，用于模拟任意长度的音频和视觉信号，同时显着降低训练期间的内存要求。我们进一步对策划数据集进行了深入的分析，并定义了开放域视听同步的评估度量。我们在标准唇读语音基准测试中应用我们的方法，LRS2和LRS3，在各个方面的消融。最后，我们在新的VGG-SOCKC SYNC视频数据集中设置了与超过160个不同类别的通用视听同步的第一个基准。在所有情况下，我们所提出的模型通过显着的保证金优于以前的最先进。

Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this end, we introduce the notion of a multimodal versatile network -a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51, Kinetics600, Audioset and ESC-50 when compared to previous self-supervised work. Our models are publicly available [1, 2, 3]. * Equal contribution. † Work done during an internship at DeepMind. 34th Conference on Neural Information Processing Systems (NeurIPS 2020),

在我们的日常生活中，视听场景是普遍存在的。对于人类来说是常见的常见地定位不同的探测物体，但是对于在没有类别注释的情况下实现类感知的声音对象本地化的机器非常具有挑战性，即，本地化声音对象并识别其类别。为了解决这个问题，我们提出了一个两阶段的逐步学习框架，以仅使用音频和视觉之间的对应方式本地化和识别复杂的视听方案中的探测对象。首先，我们建议通过单一源案例中通过粗粒化的视听对应来确定声音区域。然后，声音区域中的视觉功能被利用为候选对象表示，以建立类别表示对象字典，用于表达视觉字符提取。我们在鸡尾酒会方案中生成类感知对象本地化映射，并使用视听对应来抑制静音区域来引用此字典。最后，我们使用类别级视听一致性作为达到细粒度音频和探测物体分布对齐的监督。关于现实和综合视频的实验表明，我们的模型在本地化和识别物体方面是优越的，以及滤除静音。我们还将学习的视听网络转移到无监督的对象检测任务中，获得合理的性能。

Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contrast, are typically modality-specific and optimised for unimodal benchmarks, and hence late-stage fusion of final representations or predictions from each modality (`late-fusion') is still a dominant paradigm for multimodal video classification. Instead, we introduce a novel transformer based architecture that uses `fusion bottlenecks' for modality fusion at multiple layers. Compared to traditional pairwise self-attention, our model forces information between different modalities to pass through a small number of bottleneck latents, requiring the model to collate and condense the most relevant information in each modality and only share what is necessary. We find that such a strategy improves fusion performance, at the same time reducing computational cost. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks including Audioset, Epic-Kitchens and VGGSound. All code and models will be released.

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.

尽管事实证明，视听表征适用于许多下游任务，但舞蹈视频的表示，这是更具体的，并且总是伴随着具有复杂听觉内容的音乐，但仍然具有挑战性且没有评估。考虑到舞者和音乐节奏的节奏运动之间的内在结合，我们介绍了Mudar，这是一个新颖的音乐舞蹈表示学习框架，以明确和隐性的方式执行音乐和舞蹈节奏的同步。具体而言，我们根据音乐节奏分析启发的视觉外观和运动提示得出舞蹈节奏。然后，视觉节奏在时间上与音乐对应物对齐，这些音乐由声音强度的幅度提取。同时，我们利用对比度学习在音频和视觉流中隐含的节奏的隐式连贯性。该模型通过预测视听对之间的时间一致性来学习关节嵌入。音乐舞蹈表示以及检测音频和视觉节奏的能力，可以进一步应用于三个下游任务：（a）舞蹈分类，（b）音乐舞蹈检索，以及（c）音乐舞蹈重新定位。广泛的实验表明，我们提出的框架以大幅度优于其他自我监督方法。

我们介绍了空间本地化叙述中的视频中的任务。我们的方法的关键是能够学会在与随附的叙述的视频中的大型视频中对自我监督进行空间地定位与自我监督的互动。为实现这一目标，我们提出了一种多层跨模型关注网络，可以在培训期间有效优化对比损失。我们介绍了一种分割的策略，可以通过视觉和自然语言方式计算和中间模态注意力之间的交替，这允许通过直接对比两种方式的表示来实现有效的培训。我们展示了我们对HOWTO100M教学数据集的自我训练的方法的有效性，并在YouCook2 DataSet中的本地化描述交互的新收集数据集上进行评估。我们展示了我们的方法优于替代基准，包括浅薄的共同关注和完全跨越的关注。我们还将我们的方法应用于在Flickr30k上的弱监管下的图像中的接地短语，并显示堆叠多个注意层是有效的，并且当与对区域丢失相结合时，在召回召回和指向时达到最先进的艺术状态手准确性。

我们提出了一种为给定视频推荐音乐曲目的方法，反之亦然，基于它们的时间对齐及其在艺术层面上的信件。我们提出了一种自我监督的方法，该方法直接从数据中学习了这一对应，而无需任何人类注释。为了捕获解决任务所需的高级概念，我们建议使用每种模式的变压器网络对视频和音乐信号的长期时间上下文进行建模。实验表明，这种方法强烈胜过不利用时间上下文的替代方案。我们的贡献的结合提高了先前最高现状的检索准确性高达10倍。这种强大的改进使我们能够引入广泛的分析和应用。例如，我们可以根据视觉定义的属性来调节音乐检索。

我们建议探索一个称为视听分割（AVS）的新问题，其中的目标是输出在图像帧时产生声音的对象的像素级映射。为了促进这项研究，我们构建了第一个视频分割基准（AVSBENCH），为声音视频中的声音对象提供像素的注释。使用此基准测试了两个设置：1）具有单个声源的半监督音频分割和2）完全监督的音频段段，并带有多个声源。为了解决AVS问题，我们提出了一种新颖的方法，该方法使用时间像素的视听相互作用模块注入音频语义作为视觉分割过程的指导。我们还设计正规化损失，以鼓励训练期间的视听映射。 AVSBench上的定量和定性实验将我们的方法与相关任务中的几种现有方法进行了比较，这表明所提出的方法有望在音频和像素视觉语义之间建立桥梁。代码可从https://github.com/opennlplab/avsbench获得。

我们介绍了一种视听方法，用于远程文本到视频检索。与以前专为简短视频检索设计的方法（例如，持续时间为5-15秒）不同，我们的方法旨在检索捕获复杂人类动作的长时间视频。仅标准视频方法的一个挑战是与从这样的长视频中处理数百个密集提取的帧相关的大量计算成本。为了解决这个问题，我们建议用紧凑的音频提示替换视频的部分，这些线索简洁地汇总了动态音频事件，并且处理便宜。我们的方法称为Eclipse（带有声音编码的有效剪辑），通过添加一个统一的视听变压器块，将流行的剪辑模型调整为视听视频设置，该块从视频和音频流中捕获互补的提示。除了比仅长期视频的方法快2.92倍和2.34倍的内存效率外，我们的方法还可以在几个不同的远程视频数据集上，例如ActivityNet，QVHighighlights，Youcoook2，Youcoook2，Youcook2，Youcook2，Youcook2，Youcook2，Youcook2，Youcook2， Didemo和Charades。

我们提出了一个简单而有效的自我监督框架，用于视听表示学习，以将声源定位在视频中。为了了解什么使能够学习有用的表示形式，我们系统地研究了数据增强的效果，并揭示（1）数据增强的组成起着关键作用，{\ em I.E.}〜明确鼓励音频表征是不变的各种转换〜（{\ em转换不变性}）; （2）强制执行几何一致性基本上提高了学会表示的质量，{\ em，即}〜所检测到的声源应遵循在输入视频帧〜（{\ em em transive equivarianciance}）上应用的相同转换。广泛的实验表明，我们的模型在两个声音定位基准上的先前方法（即Flickr-soundnet和vgg-sounds）都显着优于先前的方法。此外，我们还评估了音频检索和跨模式检索任务。在这两种情况下，我们的自我监管模型都表现出了出色的检索性能，甚至在音频检索中具有监督方法竞争。这揭示了所提出的框架学会了强大的多模式表示，这些表示有益于声音定位和对进一步应用的概括。 \ textIt {所有代码都将可用}。

The thud of a bouncing ball, the onset of speech as lips open -when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals. In this paper, we argue that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation. We propose to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned. We use this learned representation for three applications: (a) sound source localization, i.e. visualizing the source of sound in a video; (b) audio-visual action recognition; and (c) on/offscreen audio source separation, e.g. removing the off-screen translator's voice from a foreign official's speech. Code, models, and video results are available on our webpage: http://andrewowens.com/multisensory.

We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a graph in which images and separated sounds correspond to nodes, and train a random walker to transition between nodes from different modalities with high return probability. The transition probabilities for this walk are determined by an audio-visual similarity metric that is learned by our model. We show through experiments with musical instruments and human speech that our model can successfully localize multiple sounds, outperforming other self-supervised methods. Project site: https://hxixixh.github.io/mix-and-localize

The remarkable success of deep learning in various domains relies on the availability of large-scale annotated datasets. However, obtaining annotations is expensive and requires great effort, which is especially challenging for videos. Moreover, the use of human-generated annotations leads to models with biased learning and poor domain generalization and robustness. As an alternative, self-supervised learning provides a way for representation learning which does not require annotations and has shown promise in both image and video domains. Different from the image domain, learning video representations are more challenging due to the temporal dimension, bringing in motion and other environmental dynamics. This also provides opportunities for video-exclusive ideas that advance self-supervised learning in the video and multimodal domain. In this survey, we provide a review of existing approaches on self-supervised learning focusing on the video domain. We summarize these methods into four different categories based on their learning objectives: 1) pretext tasks, 2) generative learning, 3) contrastive learning, and 4) cross-modal agreement. We further introduce the commonly used datasets, downstream evaluation tasks, insights into the limitations of existing works, and the potential future directions in this area.

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。

我们介绍了Audioscopev2，这是一种最先进的通用音频视频在屏幕上的声音分离系统，该系统能够通过观看野外视频来学习将声音与屏幕上的对象相关联。我们确定了先前关于视听屏幕上的声音分离的几个局限性，包括对时空注意力的粗略分辨率，音频分离模型的收敛性不佳，培训和评估数据的差异有限，以及未能说明贸易。在保存屏幕声音和抑制屏幕外声音之间的关闭。我们为所有这些问题提供解决方案。我们提出的跨模式和自我发场网络体系结构随着时间的推移以精细的分辨率捕获了视听依赖性，我们还提出了有效的可分离变体，这些变体能够扩展到更长的视频而不牺牲太多性能。我们还发现，仅在音频上进行预训练模型可大大改善结果。为了进行培训和评估，我们从大型野外视频数据库（YFCC100M）中收集了新的屏幕上的人类注释。这个新数据集更加多样化和具有挑战性。最后，我们提出了一个校准过程，该过程允许对屏幕重建与屏幕外抑制进行精确调整，从而大大简化了具有不同操作点的模型之间的性能。总体而言，我们的实验结果表明，在屏幕上的分离性能在更一般条件下的屏幕分离性能的改善要比以前具有最小的额外计算复杂性的方法更为普遍。