標題: Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur [打印本頁] 作者: VER 時間: 2025-3-21 18:12
書目名稱Computer Vision – ECCV 2020影響因子(影響力)
書目名稱Computer Vision – ECCV 2020影響因子(影響力)學科排名
書目名稱Computer Vision – ECCV 2020網(wǎng)絡公開度
書目名稱Computer Vision – ECCV 2020網(wǎng)絡公開度學科排名
書目名稱Computer Vision – ECCV 2020被引頻次
書目名稱Computer Vision – ECCV 2020被引頻次學科排名
書目名稱Computer Vision – ECCV 2020年度引用
書目名稱Computer Vision – ECCV 2020年度引用學科排名
書目名稱Computer Vision – ECCV 2020讀者反饋
書目名稱Computer Vision – ECCV 2020讀者反饋學科排名
作者: Tdd526 時間: 2025-3-21 22:57 作者: octogenarian 時間: 2025-3-22 02:51 作者: 加強防衛(wèi) 時間: 2025-3-22 08:24
Simplicial Complex Based Point Correspondence Between Images Warped onto Manifolds,cess of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although, currently, several existing methods “flatten” such 3D images to use planar graph/hypergraph matching methods, they still suffer f作者: 啞巴 時間: 2025-3-22 11:39 作者: dura-mater 時間: 2025-3-22 16:09
Distance-Normalized Unified Representation for Monocular 3D Object Detection,bject detection, we introduce a single-stage and multi-scale framework to learn a unified representation for objects within different distance ranges, termed as UR3D. UR3D formulates different tasks of detection by exploiting the scale information, to reduce model capacity requirement and achieve ac作者: dura-mater 時間: 2025-3-22 18:35 作者: 黑豹 時間: 2025-3-22 23:36
Where to Explore Next? ExHistCNN for History-Aware Autonomous 3D Exploration,imation of the Next Best View (NBV) that maximises the coverage of the unknown area. We do this by re-formulating NBV estimation as a classification problem and we propose a novel learning-based metric that encodes both, the current 3D observation (a depth frame) and the history of the ongoing recon作者: ALOFT 時間: 2025-3-23 03:06
Semi-supervised Segmentation Based on Error-Correcting Supervision,oped recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation n作者: Progesterone 時間: 2025-3-23 07:04 作者: 方舟 時間: 2025-3-23 13:45
Label-Similarity Curriculum Learning,ch for image classification that adapts the loss function by changing the label representation..The idea is to use a probability distribution over classes as target label, where the class probabilities reflect the similarity to the true class. Gradually, this label representation is shifted towards 作者: geometrician 時間: 2025-3-23 14:21 作者: Instantaneous 時間: 2025-3-23 20:02 作者: 拍翅 時間: 2025-3-24 01:50 作者: 預兆好 時間: 2025-3-24 04:39 作者: 剛毅 時間: 2025-3-24 06:42
Differentiable Joint Pruning and Quantization for Hardware Efficiency,roblem, trading off between model pruning and quantization automatically for hardware efficiency. DJPQ incorporates variational information bottleneck based structured pruning and mixed-bit precision quantization into a single differentiable loss function. In contrast to previous works which conside作者: relieve 時間: 2025-3-24 12:14 作者: CANE 時間: 2025-3-24 16:19
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Usindate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) guided reconstructions to acquire training data. Our method runs efficiently on a mobile device and outperforms existing learned and hand-designed fe作者: Systemic 時間: 2025-3-24 20:18 作者: Virtues 時間: 2025-3-25 00:58 作者: BAIL 時間: 2025-3-25 06:20
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: Myocyte 時間: 2025-3-25 09:49 作者: ARCH 時間: 2025-3-25 14:16
Sequential Deformation for Accurate Scene Text Detection,diction. The whole network can be easily optimized through an end-to-end multi-task manner. Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods.作者: Onerous 時間: 2025-3-25 19:19 作者: Lacerate 時間: 2025-3-25 22:36 作者: 消毒 時間: 2025-3-26 01:03
https://doi.org/10.1007/978-94-011-4102-4ate outputs are incorporated into an optimization framework that also includes physically plausible regularizers, in order to retrieve the 3D velocity field. Extensive experiments on both simulated and real data demonstrate the efficacy of our approach.作者: 一再遛 時間: 2025-3-26 06:48
I. Pavlik,J.O. Falkinham III,J. Kazdaof the projected 2D corners and centers of 3D boxes, which can be used to recover object physical size and orientation by a projection-consistency loss. Experimental results on the challenging KITTI autonomous driving dataset show that UR3D achieves accurate monocular 3D object detection with a compact architecture.作者: SSRIS 時間: 2025-3-26 11:54
https://doi.org/10.1007/978-3-030-72854-0N, that estimates the NBV as a set of directions towards which the depth sensor finds most unexplored areas. We perform extensive evaluation on both synthetic and real room scans demonstrating that the proposed ExHistCNN is able to approach the exploration performance of an oracle using the complete knowledge of the 3D environment.作者: 修正案 時間: 2025-3-26 14:41
Micael Jonsson,Ryan A. SponsellerUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages. Experiments indicate that QSQS improves mean average precision from 74.20% to 75.11% for PASCAL VOC 2007. It consistently outperforms NMS and soft-NMS for . subset of benchmark pedestrian detection CityPersons.作者: 使顯得不重要 時間: 2025-3-26 17:03
Secondary Metabolism of Predatory Bacteria,esGAN can provide visually appealing results in the eyeglasses-removed face images even for semi-transparent color eyeglasses or glasses with glare. Furthermore, we demonstrate significant improvement in face recognition accuracy for face images with glasses by applying our method as a pre-processing step in our face recognition experiment.作者: 簡潔 時間: 2025-3-26 21:27
Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction,ate outputs are incorporated into an optimization framework that also includes physically plausible regularizers, in order to retrieve the 3D velocity field. Extensive experiments on both simulated and real data demonstrate the efficacy of our approach.作者: calorie 時間: 2025-3-27 02:24
Distance-Normalized Unified Representation for Monocular 3D Object Detection,of the projected 2D corners and centers of 3D boxes, which can be used to recover object physical size and orientation by a projection-consistency loss. Experimental results on the challenging KITTI autonomous driving dataset show that UR3D achieves accurate monocular 3D object detection with a compact architecture.作者: arboretum 時間: 2025-3-27 06:53
Where to Explore Next? ExHistCNN for History-Aware Autonomous 3D Exploration,N, that estimates the NBV as a set of directions towards which the depth sensor finds most unexplored areas. We perform extensive evaluation on both synthetic and real room scans demonstrating that the proposed ExHistCNN is able to approach the exploration performance of an oracle using the complete knowledge of the 3D environment.作者: mechanism 時間: 2025-3-27 11:42 作者: Cardioplegia 時間: 2025-3-27 15:35 作者: ULCER 時間: 2025-3-27 18:08 作者: TIGER 時間: 2025-3-27 22:09
Stream Regulation in North Americadate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) guided reconstructions to acquire training data. Our method runs efficiently on a mobile device and outperforms existing learned and hand-designed feature descriptors for this task.作者: Countermand 時間: 2025-3-28 04:05 作者: 北極人 時間: 2025-3-28 10:17
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Usindate the inherent differences in appearance between real images and DEMs, we train a cross-domain feature descriptor using Structure From Motion (SFM) guided reconstructions to acquire training data. Our method runs efficiently on a mobile device and outperforms existing learned and hand-designed feature descriptors for this task.作者: Original 時間: 2025-3-28 14:12 作者: 柔聲地說 時間: 2025-3-28 16:23
978-3-030-58525-9Springer Nature Switzerland AG 2020作者: 樣式 時間: 2025-3-28 21:32
M. D. Amarasinghe,S. Balasubramaniame available as ground truths. Recently, there have been some approaches that incorporate the problem setting of non-rigid structure-from-motion (NRSfM) into deep learning to learn 3D structure reconstruction. The most important difficulty of NRSfM is to estimate both the rotation and deformation at 作者: 非秘密 時間: 2025-3-29 00:09 作者: jabber 時間: 2025-3-29 04:03
https://doi.org/10.1007/978-94-011-4102-4 high-resolution images at high frame rates, which generates bandwidth and memory issues. By capturing only changes in the brightness with a very low latency and at low data rate, event-based cameras have the ability to tackle such issues. In this paper, we present a new framework that retrieves den作者: 防水 時間: 2025-3-29 10:46
https://doi.org/10.1007/978-94-011-4102-4cess of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although, currently, several existing methods “flatten” such 3D images to use planar graph/hypergraph matching methods, they still suffer f作者: 標準 時間: 2025-3-29 15:25 作者: A簡潔的 時間: 2025-3-29 16:53
I. Pavlik,J.O. Falkinham III,J. Kazdabject detection, we introduce a single-stage and multi-scale framework to learn a unified representation for objects within different distance ranges, termed as UR3D. UR3D formulates different tasks of detection by exploiting the scale information, to reduce model capacity requirement and achieve ac作者: 大炮 時間: 2025-3-29 21:41
https://doi.org/10.1007/978-3-030-72854-0versity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to achieve accurate scene text detection is still an open problem. In this paper, we propose a novel sequential deformation method t作者: 啤酒 時間: 2025-3-30 03:13 作者: 植物學 時間: 2025-3-30 04:51
Micael Jonsson,Ryan A. Sponselleroped recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation n作者: aqueduct 時間: 2025-3-30 08:57 作者: needle 時間: 2025-3-30 16:12 作者: KEGEL 時間: 2025-3-30 18:27 作者: 愛社交 時間: 2025-3-30 21:42 作者: Gentry 時間: 2025-3-31 04:07 作者: 鉆孔 時間: 2025-3-31 06:19
Secondary Metabolism of Predatory Bacteria, position of eyeglasses and then remove them from face images. Our ByeGlassesGAN consists of an encoder, a face decoder, and a segmentation decoder. The encoder is responsible for extracting information from the source face image, and the face decoder utilizes this information to generate glasses-re作者: 揉雜 時間: 2025-3-31 10:41
Kendall Cotton Bronk,Caleb Mitchellroblem, trading off between model pruning and quantization automatically for hardware efficiency. DJPQ incorporates variational information bottleneck based structured pruning and mixed-bit precision quantization into a single differentiable loss function. In contrast to previous works which conside作者: parasite 時間: 2025-3-31 13:26
Anthony L. Burrow,Patrick L. Hill we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions. Although 3D facial generative models have been widely explored during the past years, 4D animation remains relatively unexplored. To this end, in this study we作者: ADORE 時間: 2025-3-31 21:26 作者: 描繪 時間: 2025-4-1 01:18 作者: 甜瓜 時間: 2025-4-1 04:27
Procrustean Regression Networks: Learning 3D Structure of Non-rigid Objects from 2D Annotations,ingle-frame basis. The proposed method can handle inputs with missing entries and experimental results validate that the proposed framework shows superior reconstruction performance to the state-of-the-art method on the Human 3.6M, 300-VW, and SURREAL datasets, even though the underlying network str作者: 不斷的變動 時間: 2025-4-1 09:14 作者: 笨重 時間: 2025-4-1 12:59
Simplicial Complex Based Point Correspondence Between Images Warped onto Manifolds,s of graphs. We propose a constrained quadratic assignment problem (QAP) that matches each .-skeleton of the simplicial complexes, iterating from the highest to the lowest dimension. The accuracy and robustness of our approach are illustrated on both synthetic and real-world spherical/warped (projec
