In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is available at https://github.com/DeepMed-Lab-ECNU/BCP}.
Modern object detection and instance segmentation networks stumble when picking out humans in crowded or highly occluded scenes. Yet, these are often scenarios where we require our detectors to work well. Many works have approached this problem with model-centric improvements. While they have been shown to work to some extent, these supervised methods still need sufficient relevant examples (i.e. occluded humans) during training for the improvements to be maximised. In our work, we propose a simple yet effective data-centric approach, Occlusion Copy & Paste, to introduce occluded examples to m…
Google Research（谷歌研究院）；UC Berkeley（加州大学伯克利分校）；Cornell University（康奈尔大学）
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation ([13, 12]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid ga…
Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective of data augmentation. Primarily, a particular copy-paste scheme is proposed towards making crowded scenes. Based on this operation, we first design a "consensus learning" method to further resist the ICD problem and then find out the pastin…
Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation. In this work, we propose a pixel-wise contrastive learning method called CP2 (Copy-Paste Contrastive Pretraining), which facilitates both image- and pixel-level representation learning and therefore is more suitable for downstream dense prediction tasks. In detail, we copy-paste a random crop from an image (the…
Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家医疗数据科学研究院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Ministry of Education), Southeast University（东南大学新一代人工智能技术及交叉应用教育部重点实验室）；School of Data and Computer Science, Shandong Women’s University（山东女子学院数据与计算机科学学院）
DIVUS corp.（DIVUS公司）；DeltaX corp.（DeltaX公司）；DP World（DP世界公司）；Kumoh National Institute of Technology（庆熙大学（注：此处原机构名称为Kumoh National Institute of Technology，常见中文名应为“公州大学”，但根据要求保留原文并翻译，此处可能存在翻译准确性问题，需注意））
Copy-Paste has proven to be a very effective data augmentation for instance segmentation which can improve the generalization of the model. We used a task-specific Copy-Paste data augmentation method to achieve good performance on the instance segmentation track of the 2nd VIPriors workshop challenge. We also applied additional data augmentation techniques including RandAugment and GridMask. Our segmentation model is the HTC detector on the CBSwin-B with CBFPN with some tweaks. This model was trained at the multi-scale mode by a random sampler on the 6x schedule and tested at the single-scale …
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
Stanford University（斯坦福大学）；University of Southern California（南加州大学）；Bosch Research North America（博世北美研究院）；Bosch Center for Artificial Intelligence (BCAI)（博世人工智能中心（BCAI））
Centralized Copy-Paste: Enhanced Data Augmentation Strategy for Wildland Fire Semantic Segmentation
Mechanical and Aerospace Engineering, AIAA Student Member（机械与航空航天工程，AIAA学生会员）；Department of Computer Science（计算机科学系）；School of Environment and Natural Resources（环境与自然资源学院）；Computer Science and Engineering（计算机科学与工程）；Department of Electronics and Telecommunications Engineering（电子与电信工程系）；Aeronautical and Astronautical Engineering, AIAA Associate Fellow（航空与航天工程，AIAA associate fellow）
Hanbat National University（韩巴国立大学）；SI Analytics；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation. However, the extension of the idea from classification to segmentation (i.e., structured output) is nontrivial. This paper systematically studies the impact of mixup under the domain adaptaive semantic segmentation task and presents a simple yet effective mixup strategy called Bidirectional Domain Mix…
In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training self-supervised depth estimation. We argue that the semantics of a coherent group of pixels in 3D space is self-contained and invariant to the contexts in which they appear. We group coherent, semantically related pixels into coherent depth regions given their estimated depth and use copy-paste to synthetically vary their contexts. In this way, cross-context correspondences are built in contrastive learning and a con…
We present a novel deep learning based algorithm for video inpainting. Video inpainting is a process of completing corrupted or missing regions in videos. Video inpainting has additional challenges compared to image inpainting due to the extra temporal information as well as the need for maintaining the temporal coherency. We propose a novel DNN-based framework called the Copy-and-Paste Networks for video inpainting that takes advantage of additional information in other frames of the video. The network is trained to copy corresponding contents in reference frames and paste them to fill the ho…
Southeast University（东南大学）；University of Technology Sydney（悉尼科技大学）；Huazhong University of Science and Technology（华中科技大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Australian National University（澳大利亚国立大学）
Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets)…
University of Bern（伯尔尼大学）；University Maryland, College Park（马里兰大学帕克分校）
In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input. Our system naturally resolves both shading and geometric inconsistencies between source and target image, resulting in a merged result image that features the content from the pasted source image, seamlessly pasted into the target context. Our framework is based on a novel training image transformation procedure that allows to train a deep convolutional neural network end-to-end to automatically learn a representat…
Harbin Institute of Technology（哈尔滨工业大学）；University of Trento（特伦托大学）；ETH Zurich（苏黎世联邦理工学院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is an impelling need, as models trained on daylight data will perform poorly at nighttime. In this paper, we study the problem of Domain Adaptive Nighttime Semantic Segmentation (DANSS), which aims to learn a discriminative nighttime model with a labeled daytime dataset and an unlabeled dataset, including coarsely aligned day-night image pairs. To this end, we propose a novel Bidirectional Mixing (Bi-Mix) framework for DANSS…
The aim of re-identification is to match objects in surveillance cameras with different viewpoints. Although ReID is developing at a considerably rapid pace, there is currently no processing method for the ReID task in multiple scenarios. However, such processing method is required in real life scenarios, such as those involving security. In the present study, a new ReID scenario was explored, which differs in terms of perspective, background, and pose(walking or cycling). Obviously, ordinary ReID processing methods cannot effectively handle such a scenario, with the introduction of image data…
The University of Adelaide（阿德莱德大学）；Alibaba Damo Academy（阿里巴巴达摩院）；Zhejiang University（浙江大学）
Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection remains largely unsolved. In this work, we present Point-Teaching, a weakly semi-supervised object detection framework to fully exploit the point annotations. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector w…
Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation
School of Software, Northwestern Polytechnical University, Shaanxi, China（西北工业大学软件学院）；Yangtze River Delta Research Institute of Northwestern Polytechnical University, Taicang, China（西北工业大学 Yangtze River Delta 研究院）；Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia（拉特罗布大学计算机科学与信息技术系）；Department of Computer Science, School of Engineering, Shantou University, Guangdong, China（汕尾大学工程学院计算机科学系）；Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong, China（山东省肿瘤医院）；Australian Institute of Health Innovation (AIHI), Macquarie University, Australia（澳大利亚健康创新研究所（AIHI），麦考瑞大学）；School of Software, Shandong University, Shandong, China（山东大学软件学院）；AIDD, Faculty of Applied Science, Macao Polytechnic University, Macao SAR, China（澳门理工学院应用科学学院AIDD）；School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China（天津大学计算机软件学院）
fortiss GmbH（福蒂斯有限公司）；Technical University of Munich（慕尼黑工业大学）
Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail. We show that the choice of starting points is indeed crucial, and that the performance of state-of-the-art attacks depends on it. First, we discuss desirable properties of starting points for attacking image classifiers, and how they can be chosen to increase query efficiency. Notably, we find that simply copying small patches from other images is a valid strategy. We then present an evaluation on ImageNet that clearl…
University of Alberta（阿尔伯塔大学）；Inception Institute of Artificial Intelligence (IIAI)（人工智能 inception 研究所）；Init ML；Polytechnic University of Catalonia, BarcelonaTech（加泰罗尼亚理工大学）
Although deep models have greatly improved the accuracy and robustness of image segmentation, obtaining segmentation results with highly accurate boundaries and fine structures is still a challenging problem. In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation prob…
The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court. And the main characteristics of this challenge are there is a high level of occlusions between players and the amount of data is quite limited. In order to address these problems, we designed a strong instance segmentation pipeline. Firstly, we employed a proper data augmentation strategy for this task mainly including photometric distortion transform and copy-paste strategy, which can generate more image i…
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective when the foreground objects have similar appearances to the background or other objects nearby. We propose a new box-supervised instance segmentation approach by developing a Semantic-aware Instance Mask (SIM) generation paradigm. Instead of heavily relying on local pair-wise affi…
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学深圳国际研究生院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framework called Uncertainty guided Cross-head Co-training (UCC) for semi-supervised semantic segmentation. Our framework introduces weak and strong augmentations within a shared encoder to achieve co-training, which naturally combines the benefits of consistency and self-training. Every segmentation head interacts with its peers and, the weak augmentation result is used for supervising the strong. The consistency training …
One of the challenges of supervised learning training is the need to procure an substantial amount of tagged data. A well-known method of solving this problem is to use synthetic data in a copy-paste fashion, so that we cut objects and paste them onto relevant backgrounds. Pasting the objects naively results in artifacts that cause models to give poor results on real data. We present a new method for cleanly pasting objects on different backgrounds so that the dataset created gives competitive performance on real data. The main emphasis is on the treatment of the border of the pasted object us…
The Hong Kong University of Science and Technology（香港科技大学）
Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth principle and performs morphology-constrained generative instance augmentation. Specifically, we propose morphology constraints that enable the augmented images to acquire luxuriant information about nuclei while maintaining their morphology characteristics (e.g., geometry and lo…
Tsinghua University（清华大学）；Peking University Shenzhen Hospital（北京大学深圳医院）
The medical datasets are usually faced with the problem of scarcity and data imbalance. Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-knowledge and time-consuming. In this paper, we propose a new object-blend method(short in soft-CP) that combines the Copy-Paste augmentation method for semantic segmentation of medical lesions offline, ensuring the correct edge information around the lession to solve the issue above-mentioned. We proved the method's validity with several datasets in different imaging modalities. In our experiments on the KiTS19[2] da…
In this contemporary world digital media such as videos and images behave as an active medium to carry valuable information across the globe on all fronts. However there are several techniques evolved to tamper the image which has made their authenticity untrustworthy. CopyMove Forgery CMF is one of the most common forgeries present in an image where a cluster of pixels are duplicated in the same image with potential postprocessing techniques. Various state-of-art techniques are developed in the recent years which are effective in detecting passive image forgery. However most methods do fail w…
Osaka University（大阪大学）；H.U. Group Research Inst. G. K.（H.U.集团研究所有限责任公司）；National Inst. of AIST（日本产业技术综合研究所）
Deep learning methods have recently exhibited impressive performance in object detection. However, such methods needed much training data to achieve high recognition accuracy, which was time-consuming and required considerable manual work like labeling images. In this paper, we automatically prepare training data using robots. Considering the low efficiency and high energy consumption in robot motion, we proposed combining robotic in-hand observation and data synthesis to enlarge the limited data set collected by the robot. We first used a robot with a depth sensor to collect images of objects…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Lenovo Corporate Research & Development, Lenovo Ltd.（联想有限公司联想企业研究院）
Deep learning models are notoriously data-hungry. Thus, there is an urging need for data-efficient techniques in medical image analysis, where well-annotated data are costly and time consuming to collect. Motivated by the recently revived "Copy-Paste" augmentation, we propose TumorCP, a simple but effective object-level data augmentation method tailored for tumor segmentation. TumorCP is online and stochastic, providing unlimited augmentation possibilities for tumors' subjects, locations, appearances, as well as morphologies. Experiments on kidney tumor segmentation task demonstrate that Tumor…
Cyberspace Research Inst. Shahid Beheshti University（沙希德贝赫什提大学网络空间研究所）
Since images are used as evidence in many cases, validation of digital images is essential. Copy-move forgery is a special kind of manipulation in which some parts of an image is copied and pasted into another part of the same image. Various methods have been proposed to detect copy-move forgery, which have achieved promising results. In previous methods, a binary mask determining the original and forged region is presented as the final result. However, it is not specified which part of the mask is the forged region. It should be noted that discriminating the original region from the duplicate…
Hazara University（哈扎拉大学）；University of Nizwa（尼兹瓦大学）
The importance and relevance of digital image forensics has attracted researchers to establish different techniques for creating as well as detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper frequency domain image manipulation method is being presented.The method exploits the localized nature of discrete wavelet transform (DWT) to get hold of the region of the host image to be manipulated. Both the patch and host image are subjected to DWT at the same level $l$ to…
This paper addresses the problem of defect segmentation in semiconductor manufacturing. The input of our segmentation is a scanning-electron-microscopy (SEM) image of the candidate defect region. We train a U-net shape network to segment defects using a dataset of clean background images. The samples of the training phase are produced automatically such that no manual labeling is required. To enrich the dataset of clean background samples, we apply defect implant augmentation. To that end, we apply a copy-and-paste of a random image patch in the clean specimen. To improve the robustness of the…
