Segment Anything 3 (SAM3) has established a powerful foundation that robustly detects, segments, and tracks specified targets in videos. However, in its original implementation, its group-level collective memory selection is suboptimal for complex multi-object scenarios, as it employs a synchronized decision across all concurrent targets conditioned on their average performance, often overlooking individual reliability. To this end, we propose SAM3-DMS, a training-free decoupled strategy that utilizes fine-grained memory selection on individual objects. Experiments demonstrate that our approach achieves robust identity preservation and tracking stability. Notably, our advantage becomes more pronounced with increased target density, establishing a solid foundation for simultaneous multi-target video segmentation in the wild.
Heilongjiang University（黑龙江大学）；Jiaxiang Industrial Technology Research Institute of HLJU（黑龙江大学嘉祥工业技术研究院）
The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-scale context information. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced, which can adaptively refine the initial pseudo labels after feature fusion to obtain high quality pseudo labels. Experiments show that the proposed DSMA framework is superior to the existing…
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. Contrary to existing approaches posing semantic segmentation as a single task of region-based classification, our algorithm decouples classification and segmentation, and learns a separate network for each task. In this architecture, labels associated with an image are identified by classification network, and binary segmentation is subsequently performed for each identified label in segmentation network. The decoupled architecture enables us to learn classification an…
Nanyang Technological University（南洋理工大学）；The University of Adelaide（阿德莱德大学）
Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In our work, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this work, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neur…
DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images
Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；Institute for Informatics, Data Science and Biostatistics（信息学、数据科学与生物统计学研究所）；Washington University School of Medicine（华盛顿大学医学院）
Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images. Methods have been proposed for eachtask with deep learning based methods heavily favored recently. However training deep learning models to solve each task separately may be sub-optimal - resource intensive and without the benefit of feature sharing. Here, we propose a new end-to-end 3D deep convolutional neural net (DCNN), called NoduleNet, to solve nodule detection, false positive reduction and nodule segmentation jointly in a multi-…
Peking University（北京大学）；Zhejiang Lab（之江实验室）；University of Oxford（牛津大学）；SenseTime Research（商汤科技研究院）
Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion. In this paper, a new paradigm for semantic segmentation is proposed. Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image. To do so, we first warp the image feature by learning a flow field to make the object part more consistent. The res…
Hainan University（海南大学）；Wuhan University of Technology（武汉理工大学）；Beijing Jiaotong University（北京交通大学）；Shenzhen University（深圳大学）；City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
Stereo superpixel segmentation aims at grouping the discretizing pixels into perceptual regions through left and right views more collaboratively and efficiently. Existing superpixel segmentation algorithms mostly utilize color and spatial features as input, which may impose strong constraints on spatial information while utilizing the disparity information in terms of stereo image pairs. To alleviate this issue, we propose a stereo superpixel segmentation method with a decoupling mechanism of spatial information in this work. To decouple stereo disparity information and spatial information, t…
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Hangzhou Dianzi University（杭州电子科技大学）；College of Media Engineering, Communication University of Zhejiang（浙江传媒学院媒体工程学院）；Shandong University（山东大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
针对牙齿 CBCT 中伪影复杂、边界模糊的问题，本文提出 FDNet，用 LF-Wavelet 增强全局语义并结合 SAM 编码器细化边界，在基准上取得最优分割性能。
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of the existing domain-invariant learning framework. (1) Being distracted by the feature distribution alignment, the network cannot focus on the segmentation task. (2) Fitting source domain data well would compromise the target domain performance. To address these issues, we propose DecoupleNet that al…
DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
Carnegie Mellon University（卡内基梅隆大学）；North Carolina A&T State University（北卡罗来纳农工州立大学）；Ho Chi Minh University of Technology（胡志明市理工大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Northwestern University（西北大学）
DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院 Cyber-Systems 研究所）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Institute of Industrial Technology Research, Zhejiang University（浙江大学工业技术研究院）；Institute of Intelligent and Software Technology, Hangzhou Dianzi University（杭州电子科技大学智能与软件技术研究所）
Late-decoupled 3D Hierarchical Semantic Segmentation with Semantic Prototype Discrimination based Bi-branch Supervision
School of Computing and AI, Southwest Jiaotong University, Chengdu, China（计算机与人工智能学院，西南交通大学，成都，中国）；Singapore University of Technology and Design, Singapore（新加坡科技设计大学，新加坡）
DiCoR: Decoupled Referent Disambiguation and Contour Recalibration for Efficient Referring Remote Sensing Image Segmentation
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与工程学院）；College of Computer Science, Nankai University（南开大学计算机学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
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）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）；Lenovo Research（联想研究院）；Lenovo Ltd.（联想有限公司）
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both …
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；The Hong Kong Polytechnic University（香港理工大学）
De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；School of School of Engineering & Physical Sciences, University of Lincoln（林肯大学工程与物理科学学院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港科学与创新研究院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；The Hong Kong Polytechnic University（香港理工大学）
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually and subjectively, at significant time and operator cost. We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality. By augmenting training images with k-space artefacts, we propose a novel CNN architecture to decouple sources of uncertainty related to the task and different k-space artefa…
University of Technology Sydney（悉尼科技大学）；Beijing Jiaotong University（北京交通大学）；Zhibian Technology Co. Ltd.（之变科技有限公司）；Zhejiang University（浙江大学）
Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving com…
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Lanzhou University（兰州大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Jinan University（济南大学）；South China University of Technology（华南理工大学）；Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
Department of Electrical Engineering（电气工程系）；AI Research Center（人工智能研究中心）；National Taiwan Ocean University（国立台湾海洋大学）；Department of Obstetrics, Gynecology（妇产科部）；Gynecology, CSMU Hospital, Taichung, Taiwan（台中市立医院妇产科）
The Hong Kong University of Science and Technology（香港科技大学）；Tencent（腾讯）
Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image semantic segmentation and video object tracking, significantly much more research effort has been made in segmentation than tracking. In this paper, we introduce "tracking-by-detection" into VOS which can coherently integrate segmentation into tracking, by proposing a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. Notably, …
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation
Device Solutions Research America, Samsung Semiconductor, Inc.（三星半导体公司美国设备解决方案研究中心）
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
Fuse4Seg: Image Fusion for Multi-Modal Medical Segmentation via Bi-level Optimization
Northwestern University（西北大学）；Northeastern University（东北大学）；South China University of Technology（华南理工大学）；Hong Kong Baptist University（香港 Baptist大学）；Beijing Normal - Hong Kong Baptist University（北京师范大学-香港 Baptist大学）
SPDCN: Strip-based Deformable Convolutional Network for Steel Surface Defect Segmentation
School of Artificial Intelligence, Jiangxi Normal University（江西师范大学人工智能学院）；Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing（江西省智能信息处理与情感计算重点实验室）
