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

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書目名稱Computer Vision –ACCV 2016影響因子(影響力)




書目名稱Computer Vision –ACCV 2016影響因子(影響力)學(xué)科排名




書目名稱Computer Vision –ACCV 2016網(wǎng)絡(luò)公開度




書目名稱Computer Vision –ACCV 2016網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision –ACCV 2016被引頻次




書目名稱Computer Vision –ACCV 2016被引頻次學(xué)科排名




書目名稱Computer Vision –ACCV 2016年度引用




書目名稱Computer Vision –ACCV 2016年度引用學(xué)科排名




書目名稱Computer Vision –ACCV 2016讀者反饋




書目名稱Computer Vision –ACCV 2016讀者反饋學(xué)科排名

暫時(shí)過來

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

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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

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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-

調(diào)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