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標(biāo)題: Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw [打印本頁(yè)]

作者: 喜悅    時(shí)間: 2025-3-21 17:59
書目名稱Computer Vision – ECCV 2018影響因子(影響力)




書目名稱Computer Vision – ECCV 2018影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ECCV 2018網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ECCV 2018網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ECCV 2018被引頻次




書目名稱Computer Vision – ECCV 2018被引頻次學(xué)科排名




書目名稱Computer Vision – ECCV 2018年度引用




書目名稱Computer Vision – ECCV 2018年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2018讀者反饋




書目名稱Computer Vision – ECCV 2018讀者反饋學(xué)科排名





作者: Paleontology    時(shí)間: 2025-3-21 21:33

作者: In-Situ    時(shí)間: 2025-3-22 00:27

作者: transdermal    時(shí)間: 2025-3-22 05:20
Pairwise Body-Part Attention for Recognizing Human-Object Interactions region. They ignore the fact that normally, human interacts with an object by using some parts of the body. In this paper, we argue that different body parts should be paid with different attention in HOI recognition, and the correlations between different body parts should be further considered. T
作者: obviate    時(shí)間: 2025-3-22 11:09

作者: LATHE    時(shí)間: 2025-3-22 16:48

作者: LATHE    時(shí)間: 2025-3-22 20:33

作者: anaphylaxis    時(shí)間: 2025-3-22 23:20
Learning Class Prototypes via Structure Alignment for Zero-Shot Recognitione semantic information and auxiliary datasets. Recently most . approaches focus on learning visual-semantic embeddings to transfer knowledge from the auxiliary datasets to the novel classes. However, few works study whether the semantic information is discriminative or not for the recognition task.
作者: 裁決    時(shí)間: 2025-3-23 03:24

作者: 綁架    時(shí)間: 2025-3-23 06:07
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNsill suffer from heavy noises which limit their applications. Although plenty of progresses have been made to reduce the noises and boost geometric details, due to the inherent illness and the real-time requirement, the problem is still far from been solved. We propose a cascaded Depth Denoising and
作者: Itinerant    時(shí)間: 2025-3-23 10:07
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributeser, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts. To address these limitations, we propose a novel model which rece
作者: Anemia    時(shí)間: 2025-3-23 14:27
Dynamic Filtering with Large Sampling Field for ConvNetsdentical position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. Such multiple samples enlarge the kernels’ receptive fields significantly without requiring
作者: GREG    時(shí)間: 2025-3-23 21:44
Pose Guided Human Video Generationper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a
作者: 拱形面包    時(shí)間: 2025-3-23 22:18

作者: Concrete    時(shí)間: 2025-3-24 05:50
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimationion tasks. TRL can recursively refine the results of both tasks through serialized task-level interactions. In order to mutually-boost for each other, we encapsulate the interaction into a specific Task-Attentional Module (TAM) to adaptively enhance some counterpart patterns of both tasks. Further,
作者: 擦掉    時(shí)間: 2025-3-24 07:54
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkearning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a . upon a resi
作者: reperfusion    時(shí)間: 2025-3-24 11:17

作者: Foment    時(shí)間: 2025-3-24 16:21
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applicationsisting algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct
作者: 夾克怕包裹    時(shí)間: 2025-3-24 21:26

作者: Obvious    時(shí)間: 2025-3-24 23:55

作者: MULTI    時(shí)間: 2025-3-25 05:14

作者: 商談    時(shí)間: 2025-3-25 09:08
Breivik in a Comparative Perspective,be performed in different spaces by the simple nearest neighbor approach using the learned class prototypes. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.
作者: Ligament    時(shí)間: 2025-3-25 12:05
Michael A. Landesmann,Roberto Scazzierimay be reconstructed progressively. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method achieves state-of-the-art results for monocular depth estimation and semantic segmentation.
作者: Antioxidant    時(shí)間: 2025-3-25 16:57

作者: FRET    時(shí)間: 2025-3-25 20:31
Bayesian Semantic Instance Segmentation in Open Set Worldsimulated annealing optimization equipped with an efficient image partition sampler. We show empirically that our method is competitive with state-of-the-art supervised methods on known classes, but also performs well on unknown classes when compared with unsupervised methods.
作者: Solace    時(shí)間: 2025-3-26 03:28

作者: Jubilation    時(shí)間: 2025-3-26 05:17
stagNet: An Attentive Semantic RNN for Group Activity Recognitiones and capturing inter-group relationships. Moreover, we adopt a spatio-temporal attention model to attend to key persons/frames for improved performance. Two widely-used datasets are employed for performance evaluation, and the extensive results demonstrate the superiority of our method.
作者: 小說    時(shí)間: 2025-3-26 10:15

作者: asthma    時(shí)間: 2025-3-26 16:10
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimationmay be reconstructed progressively. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method achieves state-of-the-art results for monocular depth estimation and semantic segmentation.
作者: 走調(diào)    時(shí)間: 2025-3-26 19:32

作者: COW    時(shí)間: 2025-3-26 21:11
Conference proceedings 2018The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization;?matching and recognition; video attention; and poster sessions..
作者: chemical-peel    時(shí)間: 2025-3-27 02:13
0302-9743 missions. The papers are organized in topical?sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization;?matching and recognition; video attention; and poster sessions..978-3-030-01248-9978-3-030-01249-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
作者: Spinous-Process    時(shí)間: 2025-3-27 08:47

作者: 鴿子    時(shí)間: 2025-3-27 10:24
https://doi.org/10.1007/978-3-030-56623-4uation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at ..
作者: 共同確定為確    時(shí)間: 2025-3-27 15:12

作者: NAVEN    時(shí)間: 2025-3-27 18:19

作者: definition    時(shí)間: 2025-3-27 22:08
BOP: Benchmark for 6D Object Pose Estimationuation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at ..
作者: Parallel    時(shí)間: 2025-3-28 05:08
Recovering 3D Planes from a Single Image via Convolutional Neural Networksataset for accurate planar and non-planar classification. Experiment results demonstrate that our method significantly outperforms existing methods, both qualitatively and quantitatively. The recovered planes could potentially benefit many important visual tasks such as vision-based navigation and human-robot interaction.
作者: 不發(fā)音    時(shí)間: 2025-3-28 09:14
ExFuse: Enhancing Feature Fusion for Semantic Segmentationevel and high-level features thus significantly improve the segmentation quality by 4.0% in?total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9% mean IoU, which outperforms the previous state-of-the-art results.
作者: 費(fèi)解    時(shí)間: 2025-3-28 13:12

作者: 單調(diào)女    時(shí)間: 2025-3-28 18:14

作者: 規(guī)范就好    時(shí)間: 2025-3-28 21:50

作者: 捏造    時(shí)間: 2025-3-29 02:11

作者: 金絲雀    時(shí)間: 2025-3-29 05:13

作者: 無能力    時(shí)間: 2025-3-29 07:35

作者: Parameter    時(shí)間: 2025-3-29 12:29

作者: Senescent    時(shí)間: 2025-3-29 18:12

作者: 是剝皮    時(shí)間: 2025-3-29 21:10
https://doi.org/10.1007/978-3-030-56623-4ed many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the
作者: 新義    時(shí)間: 2025-3-30 00:04
https://doi.org/10.1007/978-3-030-56623-4tly train a deep neural network to achieve this goal. A novel plane structure-induced loss is proposed to train the network to simultaneously predict a plane segmentation map and the parameters of the 3D planes. Further, to avoid the tedious manual labeling process, we show how to leverage existing
作者: 總    時(shí)間: 2025-3-30 05:06
Breivik in a Comparative Perspective,e spatio-temporal contextual information in a scene still remains a crucial yet challenging issue. We propose a novel attentive semantic recurrent neural network (RNN), dubbed as stagNet, for understanding group activities in videos, based on the .patio-.emporal .ttention and semantic .raph. A seman
作者: 遠(yuǎn)地點(diǎn)    時(shí)間: 2025-3-30 10:38
Breivik in a Comparative Perspective,e semantic information and auxiliary datasets. Recently most . approaches focus on learning visual-semantic embeddings to transfer knowledge from the auxiliary datasets to the novel classes. However, few works study whether the semantic information is discriminative or not for the recognition task.
作者: lipoatrophy    時(shí)間: 2025-3-30 16:27
Model Behavior and Sensitivity Results, from the Internet by using text queries, without any human annotation. We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling a massive amount of noisy labels and data imbalance effectively. We design a new learning curriculum by measuring the complex
作者: 柔美流暢    時(shí)間: 2025-3-30 20:01
Definitions, Terminology, and Concepts,ill suffer from heavy noises which limit their applications. Although plenty of progresses have been made to reduce the noises and boost geometric details, due to the inherent illness and the real-time requirement, the problem is still far from been solved. We propose a cascaded Depth Denoising and
作者: integral    時(shí)間: 2025-3-30 23:45
https://doi.org/10.1007/978-3-642-57408-5er, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts. To address these limitations, we propose a novel model which rece
作者: Limpid    時(shí)間: 2025-3-31 03:56

作者: 亂砍    時(shí)間: 2025-3-31 06:14
The Economy as a Chaotic Growth Oscillatorper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a
作者: 切碎    時(shí)間: 2025-3-31 12:44

作者: Density    時(shí)間: 2025-3-31 16:38
Michael A. Landesmann,Roberto Scazzieriion tasks. TRL can recursively refine the results of both tasks through serialized task-level interactions. In order to mutually-boost for each other, we encapsulate the interaction into a specific Task-Attentional Module (TAM) to adaptively enhance some counterpart patterns of both tasks. Further,
作者: 不真    時(shí)間: 2025-3-31 19:24
Michael A. Landesmann,Roberto Scazzieriearning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a . upon a resi
作者: Saline    時(shí)間: 2025-4-1 01:00
Development Economics in Perspectiveormance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level




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