Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data.
Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the \textit{Gard} site in France and the \textit{Reunion Island} in the Indian Ocean), demonstrate the significance of our proposal.
Center for Research in Computer Vision (CRCV)-University of Central Florida（中佛罗里达大学计算机视觉研究中心（CRCV））
We address the problem of novel view video prediction; given a set of input video clips from a single/multiple views, our network is able to predict the video from a novel view. The proposed approach does not require any priors and is able to predict the video from wider angular distances, upto 45 degree, as compared to the recent studies predicting small variations in viewpoint. Moreover, our method relies only onRGB frames to learn a dual representation which is used to generate the video from a novel viewpoint. The dual representation encompasses a view-dependent and a global representation…
Adobe Research（Adobe研究）；UC Berkeley（加州大学伯克利分校）；Google Research（谷歌研究）；Portland State University（波特兰州立大学）
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registratio…
DualDiff3D: Dual Structure-Appearance Diffusion Priors for Reliability-Enhanced 3D Gaussian Splatting
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Pengcheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸式媒体技术重点实验室）
Hefei University of Technology（合肥工业大学）；Zhejiang University（浙江大学）
Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e.g., rain removal and super-resolution. Stereo image restoration methods usually obtain better performance than monocular methods by learning the disparity between dual views either implicitly or explicitly. However, existing stereo rain removal methods still cannot make full use of the complementary information between two views, and we find it is because: 1) the rain streaks have more complex distributions in directions and densities, which severely damage the complementar…
Segmentation of images is a long-standing challenge in medical AI. This is mainly due to the fact that training a neural network to perform image segmentation requires a significant number of pixel-level annotated data, which is often unavailable. To address this issue, we propose a semi-supervised image segmentation technique based on the concept of multi-view learning. In contrast to the previous art, we introduce an adversarial form of dual-view training and employ a critic to formulate the learning problem in multi-view training as a min-max problem. Thorough quantitative and qualitative e…
Standard breast cancer screening involves the acquisition of two mammography X-ray projections for each breast. Typically, a comparison of both views supports the challenging task of tumor detection and localization. We introduce a deep learning, patch-based Siamese network for lesion matching in dual-view mammography. Our locally-fitted approach generates a joint patch pair representation and comparison with a shared configuration between the two views. We performed a comprehensive set of experiments with the network on standard datasets, among them the large Digital Database for Screening Ma…
A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments
Environmental Engineering The University of Memphis Memphis, TN, USA Email（环境工程 明尼苏达大学 内华达州法戈 邮箱）；Environmental Engineering North Dakota State University Fargo, ND, USA Email（环境工程 内华达州立大学 内华达州法戈 邮箱）
The bird's-eye-view (BEV) representation allows robust learning of multiple tasks for autonomous driving including road layout estimation and 3D object detection. However, contemporary methods for unified road layout estimation and 3D object detection rarely handle the class imbalance of the training dataset and multi-class learning to reduce the total number of networks required. To overcome these limitations, we propose a unified model for road layout estimation and 3D object detection inspired by the transformer architecture and the CycleGAN learning framework. The proposed model deals with…
Seoul National University（首尔大学）；Gachon University（嘉泉大学）
We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain and an estimated texture map in the texture domain. During the generating process, each domain generator exchanges features to the other by a flow-based local attention mechanism. In this manner, the proposed …
View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity
Wangxuan Institute of Computer Technology, Peking University（北京大学王萱计算机技术研究所）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Czech Academy of Sciences（捷克科学院）；Institute of Information Theory and Automation（信息理论与自动化研究所）；Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-…
M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing Anzhen Hospital, Capital Medical University（首都医科大学北京安贞医院）；Agency for Science, Technology and Research（科技研究局）
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Zhengzhou University（郑州大学）；Central South University（中南大学）；Xinjiang University（新疆大学）
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
School of Biomedical Engineering \& State Key Laboratory of Advanced Medical Materials；Devices, ShanghaiTech University, Shanghai, China Lingang Laboratory, Shanghai, China Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China Shanghai Clinical Research；Trial Center, Shanghai, China Shanghai Linkedcare Information Technology Co., Ltd., Shanghai, China
Portrait mode is widely available on smartphone cameras to provide an enhanced photographic experience. One of the primary effects applied to images captured in portrait mode is a synthetic shallow depth of field (DoF). The synthetic DoF (or bokeh effect) selectively blurs regions in the image to emulate the effect of using a large lens with a wide aperture. In addition, many applications now incorporate a new image motion attribute (NIMAT) to emulate background motion, where the motion is correlated with estimated depth at each pixel. In this work, we follow the trend of rendering the NIMAT e…
South China University of Technology（华南理工大学）；DexForce Technology Co., Ltd.（德方科技有限公司）；Magic Leap（魔力跃动）；Peng Cheng Laboratory（鹏城实验室）
Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive information from both bird's eye view (BEV) and range view (RV). These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial context; their performance i…
Many camera sensors use a dual-pixel (DP) design that operates as a rudimentary light field providing two sub-aperture views of a scene in a single capture. The DP sensor was developed to improve how cameras perform autofocus. Since the DP sensor's introduction, researchers have found additional uses for the DP data, such as depth estimation, reflection removal, and defocus deblurring. We are interested in the latter task of defocus deblurring. In particular, we propose a single-image deblurring network that incorporates the two sub-aperture views into a multi-task framework. Specifically, we …
A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition
School of Mathematics and Computational Science, Xiangtan University, National Center for Applied Mathematics in Hunan, Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education（湘潭大学数学与计算机科学学院，湖南应用数学研究中心，教育部智能计算与信息处理重点实验室）；College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学学院）；Department of Mathematics and Statistics, University of Strathclyde（斯特拉斯克莱德大学数学与统计学系）；State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学科学国家重点实验室，数学与系统科学研究院）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDA…
Institute of Information Science, Academia Sinica（中央研究院信息科学研究所）；Department of Computer Science, National Tsing Hua University（国立清华大学计算机科学系）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in shape predictions potentially due to inconsistencies between interpolated products of two images and volumes when rendering viewpoints are unknown. PADMix targets this issue with two sets of mixup procedures performed sequentially. We first perform an input mixup which, combined with a pose adaptive…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
School of Computer, National University of Defense Technology（国防科技大学计算机学院）；Academy of Military Sciences（军事科学院）；Chinese PLA General Hospital（中国人民解放军总医院）
We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object prop…
DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
In this paper, we revisit the problem of local optimization in RANSAC. Once a so-far-the-best model has been found, we refine it via Dual Principal Component Pursuit (DPCP), a robust subspace learning method with strong theoretical support and efficient algorithms. The proposed DPCP-RANSAC has far fewer parameters than existing methods and is scalable. Experiments on estimating two-view homographies, fundamental and essential matrices, and three-view homographic tensors using large-scale datasets show that our approach consistently has higher accuracy than state-of-the-art alternatives.
Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfac…
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Computer Science, Fudan University（复旦大学计算机学院）
Multi-view feature extraction is an efficient approach for alleviating the issue of dimensionality in highdimensional multi-view data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. Most CL-based methods were constructed only from the sample level. In this study, we propose a novel multiview feature extraction method based on dual contrastive head, which introduce structural-level contrastive loss into sample-level CL-based method. Structural-level CL push the potential subspace structures consistent in any two cros…
