Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

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书目名称Computer Vision –ACCV 2016影响因子(影响力)




书目名称Computer Vision –ACCV 2016影响因子(影响力)学科排名




书目名称Computer Vision –ACCV 2016网络公开度




书目名称Computer Vision –ACCV 2016网络公开度学科排名




书目名称Computer Vision –ACCV 2016被引频次




书目名称Computer Vision –ACCV 2016被引频次学科排名




书目名称Computer Vision –ACCV 2016年度引用




书目名称Computer Vision –ACCV 2016年度引用学科排名




书目名称Computer Vision –ACCV 2016读者反馈




书目名称Computer Vision –ACCV 2016读者反馈学科排名

暂时过来

Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Dataequires tremendous manual work, which is hard to scale up. Besides, the action categories in such datasets are pre-defined and vocabularies are fixed. However humans may describe the same action with different phrases, which leads to the difficulty of vocabulary expansion for traditional fully-super

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WITH

Parametric Image Segmentation of Humans with Structural Shape Priorsgrounds, articulation, varying body proportions, partial views and viewpoint changes. In this work we propose class-specific segmentation models that leverage parametric max-flow image segmentation and a large dataset of human shapes. Our contributions are as follows: (1) formulation of a sub-modula

不可救药

Lip Reading in the Wild trying to recognise a small number of utterances in controlled environments (. digits and alphabets), partially due to the shortage of suitable datasets..We make two novel contributions: first, we develop a pipeline for fully automated large-scale data collection from TV broadcasts. With this we ha

不可救药

存心

Continuous Supervised Descent Method for Facial Landmark Localisation to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by t

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Modeling Stylized Character Expressions via Deep Learningcognize the expression of humans and stylized characters independently. Then we utilize a transfer learning technique to learn the mapping from humans to characters to create a shared embedding feature space. This embedding also allows human expression-based image retrieval and character expression-

调色板

Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Unitseling approach. In particular, we introduce GP . to project multiple observed features onto a latent space, while GP . are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained