Microsoft Technology Licensing, LLC

Beijing Xiaomi Mobile Software Co., Ltd.

Kuiyuan Yang

Hao Liu

Xian-Sheng Hua

Yong Rui

Zhiwei Li

Chi Zhang

Rui Cai

Kun Guo

Wei-Ying Ma

Hong-Jiang Zhang

Chao Wang

Meng Wang

Tao Mei

Di Li

Hao Li

Pengfei Xu

Jianlong Fu

Chunyan Liu

Baoxun Wang

Zhijun Xie

A method and an apparatus for vehicle positioning. The method includes: acquiring a distance transformation image of a road marking object in a road image based on the road image of an environment where a vehicle is located at a current time point; acquiring a vector subgraph of the road marking object from a global vector map; and determining a target positioning posture of the vehicle based on the distance transformation image and the vector subgraph.

Method and apparatus for vehicle positioning

A vehicle localization method and device, an electronic device and a storage medium. The vehicle localization method includes: acquiring an image of a scene where a vehicle is located, the image comprising a semantic element; determining an element category corresponding to the semantic element; matching the semantic element with a map element to acquire a matching result by a matching method corresponding to the element category; and determining localization information of the vehicle according to the matching result.

Vehicle localization method and device, electronic device and storage medium

An image chat application generates comments to images based on features of the images. In one example, the image chat application searches through a repository of stored image-comment pairs to identify a stored image that is similar to the image, and generates a comment to the image based on an identified stored image-comment pair. In another example, the image chat application may identify and tag particular objects that dominate an image, and may generate a comment to the image based on characteristics of those particular objects. In this second example, the image chat application further generates a comment to the image based on comments previously associated with the identified tag.

Identifying image comments from similar images

Techniques and constructs to facilitate automatic tagging can provide improvements in image storage and searching. The constructs may enable training a deep network using tagged source images and target images. The constructs may also train a top layer of the deep network using a personal photo ontology. The constructs also may select one or more concepts from the ontology for tagging personal digital images.

Tagging personal photos with deep networks

A computing device configured to determine that one or more regions of an image are associated with a tag of the image is described herein. The computing device is further configured to determine one or more attribute tags describing at least one of the content or context of the one or more regions. Upon determining the attribute tags, the computing device associates the attribute tags with the tag to enable image searching based on the tag and attribute tags.

Tag association with image regions

Methods and systems for active image tagging are usable to build large datasets of tagged images by combining manual tagging by a user and automatic tagging by a computing device based on the manual tagging. Such tags may be used to effectively sort, organize, link, and search for images within large datasets of images. Additionally, the active image tagging may be configured to utilize a tagging game where multiple users manually tag images by playing a game on a computing device.

Active image tagging

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Beijing, China

Kuiyuan Yang