Huawei Technologies Co., Limited

Chinese Academy of Sciences

Ran He

Bing Yu

Yueying Kao

Xiang Wu

Tieniu Tan

Kaiqi Huang

Bailan Feng

Zhenan Sun

Yue Pan

Ronghua Xie

Yufeng Dai

Huifang Jiao

Qiang Rao

Yibo Hu

Guanglin Yang

Chihon Ho

Luming Fan

Embodiments of this application disclose a hybrid bonding structure and a hybrid bonding method. The hybrid bonding structure includes a first chip and a second chip. A surface of the first chip includes a first insulation dielectric and a first metal, and a first gap area exists between the first metal and the first insulation dielectric. A surface of the second chip includes a second insulation dielectric and a second metal. A surface of the first metal is higher than a surface of the first insulation dielectric. Metallic bonding is formed after the first metal is in contact with the second metal, and the first metal is longitudinally and transversely deformed in the first gap area. Insulation dielectric bonding is formed after the first insulation dielectric is in contact with the second insulation dielectric.

Hybrid bonding structure and hybrid bonding method

A method and an apparatus for generating a face rotation image are provided. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, wherein presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, wherein the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.

Method and apparatus for generating face rotation image

The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization. The present application can be applied to many fields relating to image aesthetic quality assessment, including image retrieval, photography and album management, etc.

Method for assessing aesthetic quality of natural image based on multi-task deep learning

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Beijing, China

Ran He