Self-attention is of vital importance in semantic segmentation as it enables modeling of long-range context, which translates into improved performance. We argue that it is equally important to model short-range context, especially to tackle cases where not only the regions of interest are small and ambiguous, but also when there exists an imbalance between the semantic classes. To this end, we propose Masked Supervised Learning (MaskSup), an effective single-stage learning paradigm that models both short- and long-range context, capturing the contextual relationships between pixels via random masking. Experimental results demonstrate the competitive performance of MaskSup against strong baselines in both binary and multi-class segmentation tasks on three standard benchmark datasets, particularly at handling ambiguous regions and retaining better segmentation of minority classes with no added inference cost. In addition to segmenting target regions even when large portions of the input are masked, MaskSup is also generic and can be easily integrated into a variety of semantic segmentation methods. We also show that the proposed method is computationally efficient, yielding an improved performance by 10\% on the mean intersection-over-union (mIoU) while requiring $3\times$ less learnable parameters.
Hebei University of Technology（河北工业大学）；Shenzhen University（深圳大学）；TU Wien（维也纳技术大学）；University of Oxford（牛津大学）
Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides, their fixed high masking strategy limits the upper bound of conditional mutual information, and the gradient noise is considerable, making less the learned representation information. Motivated by these limitations, in this paper, we propose masked patches selection and adaptive masking strategy ba…
SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation
1 Department of Computer Science；Engineering, Nanjing University of Aeronautics；\ , Nanjing, China 2 School of Biomedical Engineering；the Institute of Medical Robotics, Shanghai JiaoTong University, Shanghai, 200240, China
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Science（中国科学院大学人工智能学院）；University of Science and Technology Beijing（北京科技大学）
Electronics and Telecommunications Research Institute (ETRI)（电子与电信研究所）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；AITRICS
Masked image modeling (MIM) has become a popular strategy for self-supervised learning~(SSL) of visual representations with Vision Transformers. A representative MIM model, the masked auto-encoder (MAE), randomly masks a subset of image patches and reconstructs the masked patches given the unmasked patches. Concurrently, many recent works in self-supervised learning utilize the student/teacher paradigm which provides the student with an additional target based on the output of a teacher composed of an exponential moving average (EMA) of previous students. Although common, relatively little is …
Institute of Automation, CAS（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；SenseTime Research（商汤科技研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual self-supervised learning. Meanwhile, previous methods only consider the high-level feature and learning representation from a global perspective, which may fail to transfer to the downstream dense prediction tasks focusing on local features. In this paper, we present a novel Masked Self-supervised Transformer approach named MST, which can explicitly capture the local context of an image while preserving the global s…
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（集成空天地海大数据应用技术国家工程实验室，计算机科学与工程学院，西北工业大学）
Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Monash University（莫纳什大学）；Nile University（尼罗河大学）；Nanyang Technological University（南洋理工大学）
The computer-assisted radiologic informative report is currently emerging in dental practice to facilitate dental care and reduce time consumption in manual panoramic radiographic interpretation. However, the amount of dental radiographs for training is very limited, particularly from the point of view of deep learning. This study aims to utilize recent self-supervised learning methods like SimMIM and UM-MAE to increase the model efficiency and understanding of the limited number of dental radiographs. We use the Swin Transformer for teeth numbering, detection of dental restorations, and insta…
HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation
Medical College of Tianjin University（天津大学医学院）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Hong Kong（香港大学）；Haihe Hospital, Tianjin University（天津大学海河医院）；College of Computing and Intelligence, Tianjin University（天津大学计算与智能学院）
Facebook AI Research (FAIR)（脸书人工智能研究院）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）
Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to…
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Shanghai Jiao Tong University（上海交通大学）；University of Washington（华盛顿大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Tsinghua University（清华大学）；Stanford University（斯坦福大学）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research（微软研究院）
Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on various downstream tasks. In this work, we propose a unified view of masked image modeling after revisiting existing methods. Under the unified view, we introduce a simple yet effective method, termed as MaskDistill, which reconstructs normalized semantic features from teacher models at the masked positions, conditioning on corrupted input images. Experimental results on image classification and semantic segmentation show …
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspiration from autoregressive generative models that predict the current pixel from past pixels in a raster-scan ordering created with masked convolutions, we propose to use different orderings over the inputs using various forms of masked convolutions to construct different views of the data. For a given input, the model produces a pair of predictions with two valid orderings, and is then trained to maximize the mutual inf…
MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室，吉林大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；National Engineering Research Center of Visual Technology, School of Computer Science, Peking University（视觉技术国家工程研究中心，北京大学计算机科学学院）
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains. In contrast to discrete tokens in natural languages, the input for image MAE is continuous and subject to additional specifications. We systematically study each input specification during the pre-training stage, and find sequence length is a key axis that further scales MAE. Our study leads to a long-sequence version of MAE with minimal changes to the original recipe, by just decoupling the mask size from the patch size. For object detection and semantic segmentation, our l…
Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches. Among all the augmentation methods, masking is the most general and straightforward method that has the potential to be applied to all kinds of input and requires the least amount of domain knowledge. However, masked siamese networks require particular inductive bias and practically only work well with Vision Transfor…
Dual form Complementary Masking for Domain-Adaptive Image Segmentation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Data Science Institute, Imperial College London（伦敦帝国理工学院数据科学研究院）；iFLYTEK CO., LTD（iFLYTEK公司）
Picsart AI Research (PAIR)（Picsart人工智能研究院（PAIR））；University of Oregon（俄勒冈大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；IIT Roorkee（印度理工学院鲁尔基分校）
Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach leaves out the semantic context that an image provides during the encoding stage. This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably. To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic …
Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice of augmentations for SSL depends on the target task. In this paper, we aim to learn self-supervised features that generalize well across a variety of downstream tasks (e.g., object classification, detection and instance segmentation) without knowing any task information beforehand…
Masked Representation Modeling for Domain-Adaptive Segmentation
South China University of Technology（华南理工大学）；School of Electronic and Information Engineering, Wuyi University（武夷大学电子与信息工程学院）；South China Agricultural University（华南农业大学）
Vision Transformer (ViT) suffers from data scarcity in semi-supervised learning (SSL). To alleviate this issue, inspired by masked autoencoder (MAE), which is a data-efficient self-supervised learner, we propose Semi-MAE, a pure ViT-based SSL framework consisting of a parallel MAE branch to assist the visual representation learning and make the pseudo labels more accurate. The MAE branch is designed as an asymmetric architecture consisting of a lightweight decoder and a shared-weights encoder. We feed the weakly-augmented unlabeled data with a high masking ratio to the MAE branch and reconstru…
Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation
Institute of Innovative Research, Tokyo Institute of Technology（东京工业大学创新研究院）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）
Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel RGB values. In this paper, we propose masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning: firstly we pretrain an image (or video) model by recovering low-level features of masked patches, then we use the resulting features as targets for masked feature modeling. For …
National University of Singapore（新加坡国立大学）；ZheJiang University（浙江大学）；Peking University（北京大学）；Tencent Data Platform（腾讯数据平台）；Pengcheng Laboratory（鹏城实验室）
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud's properties, including leakage of location information and uneven information density. Concretely, we divide the input point cloud into irregular point patches and randomly mask them at a high ratio. Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tok…
HMSViT: A Hierarchical Masked Self-Supervised Vision Transformer for Corneal Nerve Segmentation and Diabetic Neuropathy Diagnosis
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；Department of Eye and Vision Sciences, University of Liverpool（眼科学与视觉科学系，利物浦大学）；Faculty of Biology, Medicine and Health, University of Manchester（生物、医学与健康学院，曼彻斯特大学）；Department of Medicine, Weill Cornell Medicine-Qatar（医学系，Weill Cornell Medicine-Qatar）
RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning
South China University of Technology（华南理工大学）；The University of Adelaide（阿德莱德大学）；The University of Western Australia（西澳大学）；The University of Sydney（悉尼大学）
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education, China（高可信软件技术教育部重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）；School of AI, Peking University（北京大学人工智能学院）；University of Hong Kong（香港大学）；School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Baidu（百度）；UC Berkeley（加州大学伯克利分校）
University of Edinburgh（爱丁堡大学）；INRIA（法国国家信息与自动化研究所）；Microsoft Research（微软研究院）
We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.
