Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estimation (HPE). To handle such occluded hand images, it is vital to effectively learn the relationship between local image features (e.g., for occluded joints) and global context (e.g., cues from inter-joints, inter-hands, or the scene). However, most current 3D HPE methods still rely on ResNet for feature extraction, and such CNN's inductive bias may not be optimal for 3D HPE due to its limited capability to model the global context. To address this limitation, we propose an effective and efficient framework for visual feature extraction in 3D HPE using recent state space modeling (i.e., Mamba), dubbed Deformable Mamba (DF-Mamba). DF-Mamba is designed to capture global context cues beyond standard convolution through Mamba's selective state modeling and the proposed deformable state scanning. Specifically, for local features after convolution, our deformable scanning aggregates these features within an image while selectively preserving useful cues that represent the global context. This approach significantly improves the accuracy of structured 3D HPE, with comparable inference speed to ResNet-50. Our experiments involve extensive evaluations on five divergent datasets including single-hand and two-hand scenarios, hand-only and hand-object interactions, as well as RGB and depth-based estimation. DF-Mamba outperforms the latest image backbones, including VMamba and Spatial-Mamba, on all datasets and achieves state-of-the-art performance.
UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection
College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Image Processing Center, Beihang University, Beijing, China（北京航空航天大学图像处理中心）；Robotics Institute, Beihang University, Beijing, China（北京航空航天大学机器人研究所）；Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China（华中科技大学同济医院）
Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
Department of Physics, J.K. Institute of Science（J.K.科学研究所物理系）；World Scientific University（世界科学出版社大学）；University of Intelligent Studies（智能研究大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）
Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark
Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院整形外科医院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Nanjing University of Science and Technology（南京理工大学）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）
DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection
Min Sun School of Materials Science and Engineering Hainan University（孙敏 海南大学材料科学与工程学院）；Fenghui Guo School of Materials Science and Engineering Hainan University（郭峰 海南大学材料科学与工程学院）
UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing Images
School of Computer Technology and Applications, Qinghai University（青海大学计算机技术与应用学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
MambaBEV: An EV-based 3D detection model with Mamba2
School of Instrument Science and Engineering, Southeast University, China（仪器科学与工程学院，东南大学，中国）；Amazon Development Center Germany GmbH, Germany（亚马逊德国开发中心，德国）；T3CAIC Technology, China（T3CAIC技术，中国）；School of Mechanical Engineering, Southeast University, China（机械工程学院，东南大学，中国）
Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation
College of Sciences, Northeastern University（东北大学理学院）；Key Laboratory of Differential Equations and Their Applications, Northeastern University（东北大学偏微分方程及其应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
University of Trento（特伦托大学）；Inria Grenoble Rhone-Alpes（法国国家信息与自动化研究所格勒诺布尔罗纳-阿尔卑斯分部）
In this paper we address the problem of generating person images conditioned on a given pose. Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose. In order to deal with pixel-to-pixel misalignments caused by the pose differences, we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 and L2 losses in order to match the details of the generated image with the target image. We test our approach using photos of persons…
VIMCAN: Visual-Inertial 3D Human Pose Estimation with Hybrid Mamba-Cross-Attention Network
Beihang University（北航）；Peng Cheng Laboratory（鹏城实验室）；Capital University of Physical Education and Sports（首都体育学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；Emory University（埃默里大学）
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition. In contrast to the state-of-the-art pose-aware networks such as CapsNet \cite{sabour2017dynamic} and STN \cite{jaderberg2015spatial}, DPNs can be naturally {\em interpreted} as an efficient solver for a challenging detection problem, namely Localized Deformable Part Models (LDPMs) where localization is introduced to DPMs as another latent variable for searching for the best poses of objects over all pixels and (predefined) scales. In particular we construct DP…
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University（中南大学机器视觉与智能医学湖南省工程研究中心）；University of Rouen-Normandy（鲁昂-诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）
University of Trento（特伦托大学）；Télécom Paris（巴黎高等电信学院）；Institut polytechnique de Paris（巴黎理工学院）
In this paper, we address the problem of generating person images conditioned on both pose and appearance information. Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa. In order to deal with pixel-to-pixel misalignments caused by the pose differences between P(xa) and P(xb), we introduce deformable skip connections in the generator of our Generative Adversarial Network. Moreover, a nearest-neighbour loss is proposed instead of the common L1 …
Robotic systems in manufacturing applications commonly assume known object geometry and appearance. This simplifies the task for the 3D perception algorithms and allows the manipulation to be more deterministic. However, those approaches are not easily transferable to the agricultural and food domains due to the variability and deformability of natural food. We demonstrate an approach applied to poultry products that allows picking up a whole chicken from an unordered bin using a suction cup gripper, estimating its pose using a Deep Learning approach, and placing it in a canonical orientation …
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging（广东省医学影像与诊断技术工程实验室）；Information Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.（南方医科大学南华医院信息中心）；Division of Spine Surgery, Department of Orthopaedics, Nanfang hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.（南方医科大学南华医院骨科脊柱外科）
Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments
Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Civil, Construction and Environmental Engineering, Iowa State University（土木、建设与环境工程系，爱荷华州立大学）
The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches focus on tracking skeletal parameters of a fixed shape model, which makes them insufficient for applications that require accurate estimates of deformable object surfaces. To overcome this limitation, we present a 3D model-based tracking system for articulated deformable objects. Our system is able to track human body pose and high resolution surface contours in real time using a commodity depth sensor and GPU hardw…
Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high dimensional state space makes it difficult to recognize, track, and manipulate deformable objects. In this paper, we introduce a predictive, model-driven approach to address this challenge, using a pre-computed, simulated database of deformable object models. Mesh models of common deformable garments are simulated with the garments picked up in multiple different poses under gravity, and stored in a database for fast and e…
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
School of Basic Medical Science, Fudan University（复旦大学基础医学院）；Academy of Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东计算机科学中心（国家济南超级计算中心））
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（工业机器人与信息学研究所（西班牙国家研究委员会-加泰罗尼亚理工大学））
Estimating the pose of an object from a monocular image is an inverse problem fundamental in computer vision. The ill-posed nature of this problem requires incorporating deformation priors to solve it. In practice, many materials do not perceptibly shrink or extend when manipulated, constituting a powerful and well-known prior. Mathematically, this translates to the preservation of the Riemannian metric. Neural networks offer the perfect playground to solve the surface reconstruction problem as they can approximate surfaces with arbitrary precision and allow the computation of differential geo…
Johns Hopkins University（约翰斯·霍普金斯大学）；Hong Kong Baptist University（香港浸会大学）
This paper addresses fully automated multi-person tracking in complex environments with challenging occlusion and extensive pose variations. Our solution combines multiple detectors for a set of different regions of interest (e.g., full-body and head) for multi-person tracking. The use of multiple detectors leads to fewer miss detections as it is able to exploit the complementary strengths of the individual detectors. While the number of false positives may increase with the increased number of bounding boxes detected from multiple detectors, we propose to group the detection outputs by boundi…
HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification
College of Mathematics and Informatics, South China Agricultural University（华南农业大学数学与信息学院）；Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs（热带南方智能农业技术重点实验室，农业部）；School of Chemistry and Chemical Engineering, South China University of Technology（华南理工大学化学与化工学院）
Center for Automation Research（自动化研究中心）；University of Maryland, College Park（马里兰大学帕克分校）
We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available unconstrained face detection datasets show that our method is able to capture the meaningful structure of faces and performs significantly better than many competitive face detection algorith…
High-Resolution Spatiotemporal Modeling with Global-Local State Space Models for Video-Based Human Pose Estimation
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China（知识驱动人机智能工程研究中心，中华人民共和国教育部，中国）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；National University of Singapore（新加坡国立大学）；Dankook University（Dankook 大学）
MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；School of Computer Science（计算机学院）；Institute of Artificial Intelligence（人工智能研究院）；Hubei Key Laboratory of Multimedia and Network Communication Engineering（湖北省多媒体与网络通信工程重点实验室）；Wuhan University（武汉大学）
