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

惡心

貪婪的人

https://doi.org/10.1007/978-94-011-7701-6equires 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

entail

Alison Gopnik,Andrew N. Meltzoffvarying illumination conditions, view angles, and surface reflectance. This is especially true for the challenging problem of pedestrian description in public spaces. We made two contributions in this study: (1) We contribute a large-scale pedestrian color naming dataset with 14,213 hand-labeled ima

颶風(fēng)

The Development of Word Meaninghowever, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speed-invariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggre

Fissure

https://doi.org/10.1057/9781137486431grounds, 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

cogitate

https://doi.org/10.1007/978-3-319-02517-9 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

節(jié)約

Myocarditis

neutral-posture

Safety Evaluation (Animal Studies),cognize 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-

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