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標(biāo)題: Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze [打印本頁]

作者: 譴責(zé)    時(shí)間: 2025-3-21 19:53
書目名稱Computer Vision – ECCV 2018 Workshops影響因子(影響力)




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




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




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




書目名稱Computer Vision – ECCV 2018 Workshops被引頻次




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




書目名稱Computer Vision – ECCV 2018 Workshops年度引用




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




書目名稱Computer Vision – ECCV 2018 Workshops讀者反饋




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





作者: 挑剔為人    時(shí)間: 2025-3-21 23:03
https://doi.org/10.1057/9780230389113ew BrandFashion dataset which is richly annotated at different granularities. Experimental results demonstrate that the proposed method is very effective in capturing a tiered similarity search space and outperforms the state-of-the-art fashion search methods.
作者: 運(yùn)動(dòng)吧    時(shí)間: 2025-3-22 00:30
English Electric — A Failure of Strategyyed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelt
作者: vertebrate    時(shí)間: 2025-3-22 05:33

作者: 文藝    時(shí)間: 2025-3-22 09:25

作者: NEX    時(shí)間: 2025-3-22 16:12

作者: NEX    時(shí)間: 2025-3-22 17:08
https://doi.org/10.1007/978-1-349-26582-4aze behavior correlates to the type of the action under execution. This information is then used to plan the leader’s actions in order to sustain the leader/follower alignment in the social interaction..The model of the leader’s gaze behavior and the alignment of the intentions is evaluated in a hum
作者: HARD    時(shí)間: 2025-3-22 23:50

作者: 品嘗你的人    時(shí)間: 2025-3-23 01:59

作者: paleolithic    時(shí)間: 2025-3-23 06:03
DesIGN: Design Inspiration from Generative Networksyed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelt
作者: 祖先    時(shí)間: 2025-3-23 13:41
Convolutional Photomosaic Generation via Multi-scale Perceptual Lossesiments that compare with a single-scale variant of the perceptual loss. We show that, overall, our approach produces visually pleasing results, providing a substantial improvement over common baselines.
作者: CEDE    時(shí)間: 2025-3-23 17:32

作者: Arthropathy    時(shí)間: 2025-3-23 19:55

作者: cylinder    時(shí)間: 2025-3-24 00:40
Action Alignment from Gaze Cues in Human-Human and Human-Robot Interactionaze behavior correlates to the type of the action under execution. This information is then used to plan the leader’s actions in order to sustain the leader/follower alignment in the social interaction..The model of the leader’s gaze behavior and the alignment of the intentions is evaluated in a hum
作者: 完全    時(shí)間: 2025-3-24 05:30
Conference proceedings 2019lected for inclusion in the proceedings. The workshop topics present a good?orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and?novel applications..
作者: 愚蠢人    時(shí)間: 2025-3-24 07:12

作者: 無意    時(shí)間: 2025-3-24 14:39
https://doi.org/10.1007/978-1-349-02479-7man motion using . [.] and make use of tailored loss functions to encourage a generative model to produce accurate future motion prediction. Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.
作者: 失誤    時(shí)間: 2025-3-24 15:46
https://doi.org/10.1007/978-3-642-76560-5module is used to ignore the unrelated features of attributes in the feature map, thus improve the similarity learning. Experiments conducted on two recent fashion datasets show that FashionSearchNet outperforms the other state-of-the-art fashion search techniques.
作者: colostrum    時(shí)間: 2025-3-24 20:13

作者: 強(qiáng)行引入    時(shí)間: 2025-3-25 00:04

作者: synovitis    時(shí)間: 2025-3-25 03:22
Astrophysics and Space Science Proceedingsrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve top-rank at the . 2018 challenge compared to elaborated models. Further, we investigate failure cases and give explanations for observed phenomena, and give some recommendations for overcoming demonstrated shortcomings.
作者: nocturia    時(shí)間: 2025-3-25 09:21
FashionSearchNet: Fashion Search with Attribute Manipulationmodule is used to ignore the unrelated features of attributes in the feature map, thus improve the similarity learning. Experiments conducted on two recent fashion datasets show that FashionSearchNet outperforms the other state-of-the-art fashion search techniques.
作者: 嚴(yán)厲譴責(zé)    時(shí)間: 2025-3-25 13:58

作者: 工作    時(shí)間: 2025-3-25 19:09
Forecasting Hands and Objects in Future Frames convolutional neural network (CNN) architecture designed for forecasting future objects given a video. The experiments confirm that our approach allows reliable estimation of future objects in videos, obtaining much higher accuracy compared to the state-of-the-art future object presence forecast method on public datasets.
作者: 惡心    時(shí)間: 2025-3-25 21:00
RED: A Simple but Effective Baseline Predictor for the , Benchmarkrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve top-rank at the . 2018 challenge compared to elaborated models. Further, we investigate failure cases and give explanations for observed phenomena, and give some recommendations for overcoming demonstrated shortcomings.
作者: 抑制    時(shí)間: 2025-3-26 03:54

作者: 大雨    時(shí)間: 2025-3-26 06:00
Strategies and Organisations of IBM and ICT localization. With the aid of the predicted landmarks, a landmark-driven attention mechanism is proposed to help improve the precision of fashion category classification and attribute prediction. Experimental results show that our approach outperforms the state-of-the-arts on the DeepFashion dataset.
作者: Pigeon    時(shí)間: 2025-3-26 08:47
https://doi.org/10.1007/978-1-349-26582-4 neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.
作者: 仇恨    時(shí)間: 2025-3-26 14:24

作者: Antimicrobial    時(shí)間: 2025-3-26 17:03

作者: 織物    時(shí)間: 2025-3-27 00:42

作者: 有花    時(shí)間: 2025-3-27 02:30

作者: ANA    時(shí)間: 2025-3-27 06:51

作者: enfeeble    時(shí)間: 2025-3-27 10:43
The Unimaginable Place in Nature,m. Our experimental results on the Cityscapes dataset present state-of-the-art semantic segmentation predictions, and instance segmentation results outperforming a strong baseline based on optical flow.
作者: 蹣跚    時(shí)間: 2025-3-27 16:30
Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversation results of diverse anime characters at 1024?.?1024 based on target pose sequences. We also create a novel dataset containing full-body 1024?.?1024 high-resolution images and exact 2D pose keypoints using Unity 3D Avatar models.
作者: 制造    時(shí)間: 2025-3-27 18:43

作者: ESPY    時(shí)間: 2025-3-27 22:29
Deep Learning for Automated Tagging of Fashion Imagess. Our prediction system hosts several classifiers working at scale to populate a catalogue of millions of products. We provide details of our models as well as the challenges involved in predicting Fashion attributes in a relatively homogeneous problem space.
作者: 傻    時(shí)間: 2025-3-28 02:07
Brand > Logo: Visual Analysis of Fashion Brandsuch as color, patterns and shapes. In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands. In particular, the activation strength and extent of neurons are studied to provide interesting insights about visual brand expressions. The propo
作者: Acetaminophen    時(shí)間: 2025-3-28 10:09

作者: Instrumental    時(shí)間: 2025-3-28 14:15

作者: 解脫    時(shí)間: 2025-3-28 15:08

作者: Oafishness    時(shí)間: 2025-3-28 18:57

作者: 貿(mào)易    時(shí)間: 2025-3-29 00:53
CRAFT: Complementary Recommendation by Adversarial Feature Transformcomplementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train a generative transformer network directl
作者: microscopic    時(shí)間: 2025-3-29 06:13
Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversacharacter images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resolution images. However, existing approaches have limitations in achieving both high image quality and structural consistency at the
作者: 闡釋    時(shí)間: 2025-3-29 10:04
Convolutional Photomosaic Generation via Multi-scale Perceptual Lossesof the mosaic collectively resemble a perceptually plausible image. In this paper, we consider the challenge of automatically generating a photomosaic from an input image. Although computer-generated photomosaicking has existed for quite some time, none have considered simultaneously exploiting colo
作者: 滲入    時(shí)間: 2025-3-29 14:54

作者: 鴕鳥    時(shí)間: 2025-3-29 16:52

作者: 敵手    時(shí)間: 2025-3-29 23:13

作者: 寡頭政治    時(shí)間: 2025-3-30 02:30

作者: 忘川河    時(shí)間: 2025-3-30 06:08
Joint Future Semantic and Instance Segmentation Predictionntly introduced towards better machine intelligence. However, predicting directly in the image color space seems an overly complex task, and predicting higher level representations using semantic or instance segmentation approaches were shown to be more accurate. In this work, we introduce a novel p
作者: 手銬    時(shí)間: 2025-3-30 09:36

作者: patriarch    時(shí)間: 2025-3-30 14:13
Convolutional Neural Network for Trajectory Predictionnd safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our mod
作者: 尖酸一點(diǎn)    時(shí)間: 2025-3-30 19:22
Action Alignment from Gaze Cues in Human-Human and Human-Robot Interactionn individuals share their intentions, it creates a social interaction that drives the mutual alignment of their actions and behavior. To understand the intentions of others, we strongly rely on the gaze cues. According to the role each person plays in the interaction, the resulting alignment of the
作者: 加劇    時(shí)間: 2025-3-30 22:49

作者: excrete    時(shí)間: 2025-3-31 03:40
978-3-030-11014-7Springer Nature Switzerland AG 2019
作者: 詞匯    時(shí)間: 2025-3-31 07:11
Computer Vision – ECCV 2018 Workshops978-3-030-11015-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
作者: Expressly    時(shí)間: 2025-3-31 12:28

作者: 錯(cuò)誤    時(shí)間: 2025-3-31 14:17
Deep Learning for Automated Tagging of Fashion Imagess. Our prediction system hosts several classifiers working at scale to populate a catalogue of millions of products. We provide details of our models as well as the challenges involved in predicting Fashion attributes in a relatively homogeneous problem space.
作者: 慢跑鞋    時(shí)間: 2025-3-31 19:16
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234200.jpg
作者: 洞穴    時(shí)間: 2025-4-1 00:01

作者: Chipmunk    時(shí)間: 2025-4-1 04:07
https://doi.org/10.1057/9780230389113uch as color, patterns and shapes. In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands. In particular, the activation strength and extent of neurons are studied to provide interesting insights about visual brand expressions. The propo




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