In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person reidentification. Code is available at: https://github. com/zhunzhong07/Random-Erasing.

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.

计算机视觉的挑战之一是它需要适应可变环境中的颜色偏差。因此，将颜色偏差对预测的不利影响最小化是视觉任务的主要目标之一。当前的解决方案着重于使用生成模型增强训练数据以增强输入变化的不变性。但是，这种方法通常会引入新的噪声，从而限制了生成数据的增益。为此，本文提出了一种策略，消除了偏差的偏差，该偏差称为随机颜色辍学（RCD）。我们的假设是，如果查询图像和画廊图像之间存在颜色偏差，那么在忽略颜色信息之后，某些示例的检索结果会更好。具体而言，该策略通过在训练数据中辍学的部分颜色信息来平衡神经网络中颜色特征和无关的特征之间的权重，以克服颜色devitaion的效果。所提出的RCD可以与各种现有的REID模型相结合而不更改学习策略，并且可以应用于其他计算机视野字段，例如对象检测。在几个REID基线和三个常见的大规模数据集（例如Market1501，Dukemtmc和MSMT17）上进行的实验已验证了该方法的有效性。跨域测试的实验表明，该策略显着消除了域间隙。此外，为了了解RCD的工作机制，我们从分类的角度分析了该策略的有效性，这表明在具有强大域变化的视觉任务中，最好利用许多而不是所有颜色信息。

The main contribution of this paper is a simple semisupervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline.We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% improvement over a strong baseline. The code is available at https://github.com/layumi/Person-reID_GAN .

Data augmentation is a widely used technique for enhancing the generalization ability of convolutional neural networks (CNNs) in image classification tasks. Occlusion is a critical factor that affects on the generalization ability of image classification models. In order to generate new samples, existing data augmentation methods based on information deletion simulate occluded samples by randomly removing some areas in the images. However, those methods cannot delete areas of the images according to their structural features of the images. To solve those problems, we propose a novel data augmentation method, AdvMask, for image classification tasks. Instead of randomly removing areas in the images, AdvMask obtains the key points that have the greatest influence on the classification results via an end-to-end sparse adversarial attack module. Therefore, we can find the most sensitive points of the classification results without considering the diversity of various image appearance and shapes of the object of interest. In addition, a data augmentation module is employed to generate structured masks based on the key points, thus forcing the CNN classification models to seek other relevant content when the most discriminative content is hidden. AdvMask can effectively improve the performance of classification models in the testing process. The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks.

使用卷积神经网络（CNN）已经显着改善了几种图像处理任务，例如图像分类和对象检测。与Reset和Abseralnet一样，许多架构在创建时至少在一个数据集中实现了出色的结果。培训的一个关键因素涉及网络的正规化，这可以防止结构过度装备。这项工作分析了在过去几年中开发的几种正规化方法，显示了不同CNN模型的显着改进。该作品分为三个主要区域：第一个称为“数据增强”，其中所有技术都侧重于执行输入数据的更改。第二个，命名为“内部更改”，旨在描述修改神经网络或内核生成的特征映射的过程。最后一个称为“标签”，涉及转换给定输入的标签。这项工作提出了与关于正则化的其他可用调查相比的两个主要差异：（i）第一个涉及在稿件中收集的论文并非超过五年，并第二个区别是关于可重复性，即所有作品此处推荐在公共存储库中可用的代码，或者它们已直接在某些框架中实现，例如Tensorflow或Torch。

闭塞者重新识别（REID）旨在匹配遮挡人物在不同的相机视图上的整体上。目标行人（TP）通常受到非行人闭塞（NPO）和Nontarget行人（NTP）的干扰。以前的方法主要集中在忽略NTP的特征污染的同时越来越越来越多的模型对非NPO的鲁棒性。在本文中，我们提出了一种新颖的特征擦除和扩散网络（FED），同时处理NPO和NTP。具体地，我们的建议闭塞擦除模块（OEM）消除了NPO特征，并由NPO增强策略辅助，该策略模拟整体行人图像上的NPO并产生精确的遮挡掩模。随后，我们随后，我们将行人表示与其他记忆特征弥散，以通过学习的跨关注机构通过新颖的特征扩散模块（FDM）实现的特征空间中的NTP特征。随着OEM的闭塞分数的指导，特征扩散过程主要在可见的身体部位上进行，保证合成的NTP特性的质量。通过在我们提出的联邦网络中联合优化OEM和FDM，我们可以大大提高模型对TP的看法能力，并减轻NPO和NTP的影响。此外，所提出的FDM仅用作用于训练的辅助模块，并将在推理阶段中丢弃，从而引入很少的推理计算开销。遮挡和整体人员Reid基准的实验表明了美联储最先进的优越性，喂养的含量在封闭式封闭的内容上取得了86.3％的排名 - 1准确性，超过其他人至少4.7％。

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.

数据增强已被广泛用于改善深形网络的性能。提出了许多方法，例如丢弃，正则化和图像增强，以避免过度发出和增强神经网络的概括。数据增强中的一个子区域是图像混合和删除。这种特定类型的增强混合两个图像或删除图像区域以隐藏或制定困惑的图像的某些特征，以强制它强调图像中对象的整体结构。与此方法培训的模型表明，与未执行混合或删除的培训相比，该模型表现得很好。这种培训方法实现的额外福利是对图像损坏的鲁棒性。由于其最近的计算成本低，因此提出了许多图像混合和删除技术。本文对这些设计的方法提供了详细的审查，在三个主要类别中划分增强策略，切割和删除，切割和混合和混合。纸张的第二部分是评估这些方法的图像分类，微小的图像识别和对象检测方法，其中显示了这类数据增强提高了深度神经网络的整体性能。

与准确性和计算成本具有密切关系的图像分辨率在网络培训中发挥了关键作用。在本文中，我们观察到缩小图像保留相对完整的形状语义，但是失去了广泛的纹理信息。通过形状语义的一致性和纹理信息的脆弱的启发，我们提出了一个名为时间性解决方案递减的新颖培训策略。其中，我们在时域中随机将训练图像降低到较小的分辨率。在使用缩小图像和原始图像的替代训练期间，图像中的不稳定纹理信息导致纹理相关模式与正确标签之间的相关性较弱，自然强制执行模型，以更多地依赖于稳健的形状属性。符合人类决策规则。令人惊讶的是，我们的方法大大提高了卷积神经网络的计算效率。在Imagenet分类上，使用33％的计算量（随机将培训图像随机降低到112 $ \倍112美元）仍然可以将resnet-50从76.32％提高到77.71％，并使用63％的计算量（随机减少在50％时期的训练图像到112 x 112）可以改善resnet-50至78.18％。

The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10× or 100×? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between 'enormous data' and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pretraining) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-theart results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224×224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-theart classification results using a single full-image representation and no fine-tuning.The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102× faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007.In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012-achieving a mAP of 53.3%. Our approach combines two key insights:(1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also compare R-CNN to OverFeat, a recently proposed sliding-window detector based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset. Source code for the complete system is available at http://www.cs.berkeley.edu/ ˜rbg/rcnn.

We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For 300 × 300 input, SSD achieves 74.3% mAP 1 on VOC2007 test at 59 FPS on a Nvidia Titan X and for 512 × 512 input, SSD achieves 76.9% mAP, outperforming a comparable state-of-the-art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at: https://github.com/weiliu89/caffe/tree/ssd .

人员搜索统一人员检测和人重新识别（重新ID），以从全景画廊图像找到查询人员。一个主要挑战来自于不平衡的长尾人身份分布，这可以防止一步人搜索模型学习歧视性人员特征，以获得最终重新识别。但是，探索了如何解决一步人员搜索的重型不平衡的身份分布。设计用于长尾分类任务的技术，例如，图像级重新采样策略很难被有效地应用于与基于检测的多个多个多的人检测和重新ID子任务共同解决人员检测和重新ID子任务 - 框架框架。为了解决这个问题，我们提出了一个子任务主导的传输学习（STL）方法。 STL方法解决了主导的重新ID子批次的预测阶段的长尾问题，并通过转移学习来改善普试模型的一步人搜索。我们进一步设计了一个多级ROI融合池层，以提高一步人搜索的人特征的辨别能力。 Cuhk-Sysu和Prw Datasets的广泛实验证明了该方法的优越性和有效性。

很少有细粒度的分类和人搜索作为独特的任务和文学作品，已经分别对待了它们。但是，仔细观察揭示了重要的相似之处：这两个任务的目标类别只能由特定的对象细节歧视；相关模型应概括为新类别，而在培训期间看不到。我们提出了一个适用于这两个任务的新型统一查询引导网络（QGN）。QGN由一个查询引导的暹罗引文和兴奋子网组成，该子网还重新进行了所有网络层的查询和画廊功能，一个查询实习的区域建议特定于特定于特定的本地化以及查询指导的相似性子网络子网本网络用于公制学习。QGN在最近的一些少数细颗粒数据集上有所改善，在幼崽上的其他技术优于大幅度。QGN还对人搜索Cuhk-Sysu和PRW数据集进行了竞争性执行，我们在其中进行了深入的分析。

Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -from 1 example per class to 1 M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.