Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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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

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TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapesrefreshing the performance records on various standard benchmarks. However, limited by the representations (axis-aligned rectangles, rotated rectangles or quadrangles) adopted to describe text, existing methods may fall short when dealing with much more free-form text instances, such as curved text,

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Robust Image Stitching with Multiple Registrationst is also used by millions of consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, which picks a single transformation of each source image to align it to the other inputs, seam?finding, which selects a source image for each pixel in t

項目

CTAP: Complementary Temporal Action Proposal Generationral intervals in videos that are likely to contain an action. Previous methods can be divided to two groups: sliding window ranking and actionness score grouping. Sliding windows uniformly cover all segments in videos, but the temporal boundaries are imprecise; grouping based method may have more pr

ligature

Effective Use of Synthetic Data for Urban Scene Semantic Segmentationges, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images

ligature

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Linear Span Network for Object Skeleton Detectionrst re-visit the implementation of HED, the essential principle of which can be ideally described with a linear reconstruction model. Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) which introduces Linear Span Units (LSUs) to minimizes the reconstruction

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SaaS: Speed as a Supervisor for Semi-supervised Learning measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model paramete