Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications.
Thales LAS France（泰雷兹LAS法国公司）；LIGM (UMR 8049), Ecole des Ponts, Univ. Gustave Eiffel, CNRS, Marne-la-Vallée, France（LIGM（UMR 8049），巴黎高科路桥学校，古斯塔夫·埃菲尔大学，法国国家科学研究中心，马恩拉瓦莱，法国）
We tackle the problem of finding accurate and robust keypoint correspondences between images. We propose a learning-based approach to guide local feature matches via a learned approximate image matching. Our approach can boost the results of SIFT to a level similar to state-of-the-art deep descriptors, such as Superpoint, ContextDesc, or D2-Net and can improve performance for these descriptors. We introduce and study different levels of supervision to learn coarse correspondences. In particular, we show that weak supervision from epipolar geometry leads to performances higher than the stronger…
MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching
National Engineering Research Center for Multimedia Software（多媒体软件国家工程研究中心）；Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science（计算机科学学院）；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）；Lee Kong Chian School of Medicine（李光耀医学院）；Nanyang Technological University（南洋理工大学）；Ningbo Institute of Materials Technology and Engineering（宁波材料技术与工程研究所）；Chinese Academy of Sciences（中国科学院）；Institute for Infocomm Research (I 2 R)（信息与通信研究所以（I 2 R））；Agency for Science, Technology and Research (A*STAR)（科技研究局（A*STAR））
We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure purposes in robotics or autonomous vehicles applications. We use late fusion approach, where each modality is processed separately and fused in the final part of the processing pipeline. The proposed method achieves state-of-the-art performance on standard place recognition benchmarks. We also identify dominating modality problem when training a mul…
Modality-Aware Feature Matching in Visual and Vision-Language Applications: A Comprehensive Survey
School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；School of Computing and Communications, Lancaster University（兰卡斯特大学计算机与通讯学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（新加坡资讯研究院，科技研究局（A*STAR））；Department of Automation, Tsinghua University（清华大学自动化系）
Establishing correspondences is a fundamental task in variety of image processing and computer vision applications. In particular, finding the correspondences between a non-linearly deformed image pair induced by different modality conditions is a challenging problem. This paper describes a efficient but powerful image transform called local area transform (LAT) for modality-robust correspondence estimation. Specifically, LAT transforms an image from the intensity domain to the local area domain, which is invariant under nonlinear intensity deformations, especially radiometric, photometric, an…
Plant Phenomics based on imaging based techniques can be used to monitor the health and the diseases of plants and crops. The use of 3D data for plant phenomics is a recent phenomenon. However, since 3D point cloud contains more information than plant images, in this paper, we compare the performance of different keypoint detectors and local feature descriptors combinations for the plant growth stage and it's growth condition classification based on 3D point clouds of the plants. We have also implemented a modified form of 3D SIFT descriptor, that is invariant to rotation and is computationall…
We present a novel descriptor, called deep self-convolutional activations (DeSCA), designed for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. Motivated by descriptors based on local self-similarity (LSS), we formulate a novel descriptor by leveraging LSS in a deep architecture, leading to better discriminative power and greater robustness to non-rigid image deformations than state-of-the-art cross-modality descriptors. The DeSCA first computes self-convolutions over a local support window fo…
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Inria（法国国家信息与自动化研究所）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（巴黎大脑研究所 - ICM, 法国国家科学研究中心, 法国国家健康与医学研究院, 巴黎公立医院集团, 皮提耶-萨勒佩特里医院）；Technical University of Munich（慕尼黑工业大学）
Al-Hussein Bin Talal University（侯赛因·本·塔拉勒大学）；University of Mysore（迈索尔大学）
In this paper, we have developed Biometric recognition system adopting hand based modality Handvein, which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor ,LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kerne…
The rapid development of inexpensive commodity depth sensors has made keypoint detection and matching in the depth image modality an important problem in computer vision. Despite great improvements in recent RGB local feature learning methods, adapting them directly in the depth modality leads to unsatisfactory performance. Most of these methods do not explicitly reason beyond the visible pixels in the images. To address the limitations of these methods, we propose a framework ViewSynth, to jointly learn: (1) viewpoint invariant keypoint-descriptor from depth images using a proposed Contrastiv…
LIVIA, École de technologie supérieure (ÉTS)（高等技术学院图像与视频分析实验室）；Inria Paris（法国国家信息与自动化研究所巴黎中心）；Sorbonne Universités, UPMC Univ Paris 06（索邦大学，巴黎第六大学）；Inserm（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；Institut du cerveau et la moelle (ICM)（脑与脊髓研究所）
So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstructure. However, due to the lack of a framework for comparing across multiple modalities, studies based on multi-modal data remain elusive. This paper presents a multi-modal analysis of genetically-related subjects to compare and contrast the information provided by various MRI modalities. The proposed framework represents MRI scans as bags of SIFT features, and uses these features in a nearest…
ImFusion GmbH（ImFusion有限责任公司）；Technical University of Munich（慕尼黑工业大学）
Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We…
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan（伊斯法罕大学计算机工程学院人工智能系）；Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab（格勒诺布尔阿尔卑斯大学（Univ. Grenoble Alpes）、法国国家科学研究中心（CNRS）、格勒诺布尔综合理工学院（Grenoble INP）、GIPSA实验室（GIPSA-lab））
Multimodal manifold modeling methods extend the spectral geometry-aware data analysis to learning from several related and complementary modalities. Most of these methods work based on two major assumptions: 1) there are the same number of homogeneous data samples in each modality, and 2) at least partial correspondences between modalities are given in advance as prior knowledge. This work proposes two new multimodal modeling methods. The first method establishes a general analyzing framework to deal with the multimodal information problem for heterogeneous data without any specific prior know…
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich（于利希研究中心神经科学与医学研究所（INM-1））；Helmholtz AI, Research Centre Jülich（于利希研究中心亥姆霍兹人工智能研究所）；Montreal Neurological Institute and Hospital, McGill University（麦吉尔大学蒙特利尔神经学研究所及医院）；University of Wuppertal（伍珀塔尔大学）；Heinrich-Heine-University Düsseldorf（海因里希·海涅杜塞尔多夫大学）
Image and Point Clouds provide different information for robots. Finding the correspondences between data from different sensors is crucial for various tasks such as localization, mapping, and navigation. Learning-based descriptors have been developed for single sensors; there is little work on cross-modal features. This work treats learning cross-modal features as a dense contrastive learning problem. We propose a Tuple-Circle loss function for cross-modality feature learning. Furthermore, to learn good features and not lose generality, we developed a variant of widely used PointNet++ archite…
University of Trento（特伦托大学）；INRIA（法国国家信息与自动化研究所）；Columbia University（哥伦比亚大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR). While most SBIR approaches focus on extracting low- and mid-level descriptors for direct feature matching, recent works have shown the benefit of learning coupled feature representations to describe data from two related sources. However, cross-domain representation learning methods are typically cast into non-convex minimization problems that are difficult to optimize, leading to unsatisfactory performance. Inspired by self-paced learning, a learning methodology design…
This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI). This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard convolutional neural networks in a transfer-lea…
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
INSA-Lyon（里昂应用科学学院）；Université Claude Bernard Lyon 1（里昂克洛德·贝尔纳第一大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；University of Sherbrooke（舍布鲁克大学）；Sorbonne Université（索邦大学）；Hospices Civils de Lyon（里昂公立医院）
Bridging MRI and PET physiology: Untangling complementarity through orthogonal representations
Dept. of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院诊断与介入放射学及神经放射学系）；Digital Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院数字医学）；Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗流程与增强现实教席）；Bavarian Center for Cancer Research (BZKF) Augsburg, Germany（德国奥格斯堡巴伐利亚癌症研究中心）；Dept. of Nuclear Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院核医学系）；Dept. of Urology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院泌尿外科）；Center for Advanced Analytics and Predictive Sciences, University of Augsburg, Germany（德国奥格斯堡大学高级分析与预测科学中心）
FauForensics: Boosting Audio-Visual Deepfake Detection with Facial Action Units
School of Computer Science, Nanjing University of Posts and Telecommunications（南京邮电大学计算机科学学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））
State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；Institute of Cyber-Systems and Control, Zhejiang University（浙江大学网络空间安全与控制学院）
Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this paper, we propose a bi-modal place recognition method, which can extract a compound global descriptor from the two modalities, vision and LiDAR. Specifically, we first build the elevation image generated from 3D points as a structural representation. Then, we derive the correspondences between 3D points and image pixels that are further used in merging the pixel-wise visual features into the elevation map grids. In t…
TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（中国教育部智能感知与图像理解重点实验室，西安电子科技大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）
