We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.

In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-oneout learning. In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements. We apply this technique to self-supervised video representation learning where we sample subsequences from videos and ask the network to learn to predict the odd video subsequence. The odd video subsequence is sampled such that it has wrong temporal order of frames while the even ones have the correct temporal order. Therefore, to generate a odd-one-out question no manual annotation is required. Our learning machine is implemented as multi-stream convolutional neural network, which is learned end-to-end. Using odd-one-out networks, we learn temporal representations for videos that generalizes to other related tasks such as action recognition.On action classification, our method obtains 60.3% on the UCF101 dataset using only UCF101 data for training which is approximately 10% better than current stateof-the-art self-supervised learning methods. Similarly, on HMDB51 dataset we outperform self-supervised state-ofthe art methods by 12.7% on action classification task.

We propose a self-supervised spatiotemporal learning technique which leverages the chronological order of videos. Our method can learn the spatiotemporal representation of the video by predicting the order of shuffled clips from the video. The category of the video is not required, which gives our technique the potential to take advantage of infinite unannotated videos. There exist related works which use frames, while compared to frames, clips are more consistent with the video dynamics. Clips can help to reduce the uncertainty of orders and are more appropriate to learn a video representation. The 3D convolutional neural networks are utilized to extract features for clips, and these features are processed to predict the actual order. The learned representations are evaluated via nearest neighbor retrieval experiments. We also use the learned networks as the pre-trained models and finetune them on the action recognition task. Three types of 3D convolutional neural networks are tested in experiments, and we gain large improvements compared to existing self-supervised methods.

Is strong supervision necessary for learning a good visual representation? Do we really need millions of semantically-labeled images to train a Convolutional Neural Network (CNN)? In this paper, we present a simple yet surprisingly powerful approach for unsupervised learning of CNN. Specifically, we use hundreds of thousands of unlabeled videos from the web to learn visual representations.Our key idea is that visual tracking provides the supervision. That is, two patches connected by a track should have similar visual representation in deep feature space since they probably belong to the same object or object part. We design a Siamese-triplet network with a ranking loss function to train this CNN representation. Without using a single image from ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train an ensemble of unsupervised networks that achieves 52% mAP (no bounding box regression). This performance comes tantalizingly close to its ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We also show that our unsupervised network can perform competitively in other tasks such as surface-normal estimation.

Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the schema and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used image and video datasets and the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning.

Figure 1: Seeing these ordered frames from videos, can you tell whether each video is playing forward or backward? (answer below 1 ). Depending on the video, solving the task may require (a) low-level understanding (e.g. physics), (b) high-level reasoning (e.g. semantics), or (c) familiarity with very subtle effects or with (d) camera conventions. In this work, we learn and exploit several types of knowledge to predict the arrow of time automatically with neural network models trained on large-scale video datasets.

视频自我监督的学习是一项挑战的任务，这需要模型的显着表达力量来利用丰富的空间时间知识，并从大量未标记的视频产生有效的监督信号。但是，现有方法未能提高未标记视频的时间多样性，并以明确的方式忽略精心建模的多尺度时间依赖性。为了克服这些限制，我们利用视频中的多尺度时间依赖性，并提出了一个名为时间对比图学习（TCGL）的新型视频自我监督学习框架，该框架共同模拟了片段间和片段间的时间依赖性用混合图对比学习策略学习的时间表示学习。具体地，首先引入空间 - 时间知识发现（STKD）模块以基于离散余弦变换的频域分析从视频中提取运动增强的空间时间表。为了显式模拟未标记视频的多尺度时间依赖性，我们的TCGL将关于帧和片段命令的先前知识集成到图形结构中，即片段/间隙间时间对比图（TCG）。然后，特定的对比学习模块旨在最大化不同图形视图中节点之间的协议。为了为未标记的视频生成监控信号，我们介绍了一种自适应片段订购预测（ASOP）模块，它利用视频片段之间的关系知识来学习全局上下文表示并自适应地重新校准通道明智的功能。实验结果表明我们的TCGL在大规模行动识别和视频检索基准上的最先进方法中的优势。

In this paper we study the problem of image representation learning without human annotation. By following the principles of selfsupervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection. To maintain the compatibility across tasks we introduce the context-free network (CFN), a siamese-ennead CNN. The CFN takes image tiles as input and explicitly limits the receptive field (or context) of its early processing units to one tile at a time. We show that the CFN includes fewer parameters than AlexNet while preserving the same semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we learn both a feature mapping of object parts as well as their correct spatial arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. Our proposed method for learning visual representations outperforms state of the art methods in several transfer learning benchmarks.

The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics.We also introduce a new Two-Stream Inflated 3D Con-vNet (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters. We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0% on UCF-101.

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as 'pseudo ground truth' to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.

This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation. Given only a large, unlabeled image collection, we extract random pairs of patches from each image and train a convolutional neural net to predict the position of the second patch relative to the first. We argue that doing well on this task requires the model to learn to recognize objects and their parts. We demonstrate that the feature representation learned using this within-image context indeed captures visual similarity across images. For example, this representation allows us to perform unsupervised visual discovery of objects like cats, people, and even birds from the Pascal VOC 2011 detection dataset. Furthermore, we show that the learned ConvNet can be used in the R-CNN framework [21] and provides a significant boost over a randomly-initialized ConvNet, resulting in state-of-theart performance among algorithms which use only Pascalprovided training set annotations.

人类行动识别是计算机视觉中的重要应用领域。它的主要目的是准确地描述人类的行为及其相互作用，从传感器获得的先前看不见的数据序列中。识别，理解和预测复杂人类行动的能力能够构建许多重要的应用，例如智能监视系统，人力计算机界面，医疗保健，安全和军事应用。近年来，计算机视觉社区特别关注深度学习。本文使用深度学习技术的视频分析概述了当前的动作识别最新识别。我们提出了识别人类行为的最重要的深度学习模型，并分析它们，以提供用于解决人类行动识别问题的深度学习算法的当前进展，以突出其优势和缺点。基于文献中报道的识别精度的定量分析，我们的研究确定了动作识别中最新的深层体系结构，然后为该领域的未来工作提供当前的趋势和开放问题。

Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (69.4%) and UCF101 (94.2%). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices. 1

We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.

We investigate and improve self-supervision as a dropin replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other, traditional unsupervised learning methods. We build on this success and evaluate the ability of our self-supervised network in several contexts. On VOC segmentation and classification tasks, we present results that are state-of-the-art among methods not using Im-ageNet labels for pretraining representations.Moreover, we present the first in-depth analysis of selfsupervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness. This investigation is further expanded by revisiting the ImageNet pretraining paradigm, asking questions such as: How much training data is needed? How many labels are needed? How much do features change when fine-tuned? We relate these questions back to self-supervision by showing that colorization provides a similarly powerful supervisory signal as various flavors of ImageNet pretraining.

Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, kmeans, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.

我们提出了MACLR，这是一种新颖的方法，可显式执行从视觉和运动方式中学习的跨模式自我监督的视频表示。与以前的视频表示学习方法相比，主要关注学习运动线索的研究方法是隐含的RGB输入，MACLR丰富了RGB视频片段的标准对比度学习目标，具有运动途径和视觉途径之间的跨模式学习目标。我们表明，使用我们的MACLR方法学到的表示形式更多地关注前景运动区域，因此可以更好地推广到下游任务。为了证明这一点，我们在五个数据集上评估了MACLR，以进行动作识别和动作检测，并在所有数据集上展示最先进的自我监督性能。此外，我们表明MACLR表示可以像在UCF101和HMDB51行动识别的全面监督下所学的表示一样有效，甚至超过了对Vidsitu和SSV2的行动识别的监督表示，以及对AVA的动作检测。

We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network, resulting in two disjoint sub-networks. Each sub-network is trained to perform a difficult taskpredicting one subset of the data channels from another. Together, the sub-networks extract features from the entire input signal. By forcing the network to solve crosschannel prediction tasks, we induce a representation within the network which transfers well to other, unseen tasks. This method achieves state-of-the-art performance on several large-scale transfer learning benchmarks.

人类可以轻松地在不知道它们的情况下段移动移动物体。从持续的视觉观测中可能出现这种对象，激励我们与未标记的视频同时进行建模和移动。我们的前提是视频具有通过移动组件相关的相同场景的不同视图，并且右区域分割和区域流程将允许相互视图合成，其可以从数据本身检查，而无需任何外部监督。我们的模型以两个单独的路径开头：一种外观途径，其输出单个图像的基于特征的区域分割，以及输出一对图像的运动功能的运动路径。然后，它将它们绑定在称为段流的联合表示中，该分段流汇集在每个区域上的流程偏移，并提供整个场景的移动区域的总表征。通过培训模型，以最小化基于段流的视图综合误差，我们的外观和运动路径自动学习区域分割和流量估计，而不分别从低级边缘或光学流量构建它们。我们的模型展示了外观途径中对象的令人惊讶的出现，超越了从图像的零射对对象分割上的工作，从带有无监督的测试时间适应的视频移动对象分割，并通过监督微调，通过监督微调。我们的工作是来自视频的第一个真正的零点零点对象分段。它不仅开发了分割和跟踪的通用对象，而且还优于无增强工程的基于普遍的图像对比学习方法。