Baidu Online Network Technology (beijing) Co., Ltd.

International Business Machines Corporation

Legend (beijing) Limited

Jiang Tao Wang

Xiao Xi Liu

Yan Wang

Errui Ding

Nan Wang

Shilei Wen

Tian Xia

Chang Wang

Hang Xiao

Chi Xu

Peng Tang

Guang Zhu

Jianzhong Gao

A method for generating training models includes generating a preliminary training model based on a group of first images, the first images including different types of objects; processing a group of second images with the preliminary model to generate a probability array for each of the second images, the probability array indicating likelihoods that an object is a particular type of object; generating correlations between the different types of objects based on the probability arrays; generating a plurality of object groups based on the correlations, where each object group includes a plurality of different types of objects that have a relatively low correlation with the other types of objects in the same object group; and for each object group, generating a final training model based on a group of third images, the third images each including an object having an object type corresponding to one of the object types.

Defect detection using multiple models

According to the embodiments of the present invention, there is provided a method for training an image semantics annotation apparatus, comprising: a. providing a plurality of training images, wherein semantics and visual attribution description of respective training images are known; b. inputting at least part of the training images to a locator of the image semantic annotation apparatus; c. determining, by the locator, at least one local area of each input training image, and inputting the determined respective local areas into an attribute predictor of the image semantic annotation apparatus; d. obtaining a visual attribute prediction result of each input local area from the attribute predictor; e. training the locator and the attribute predictor according to the obtained visual attribute prediction result of each local area and a known visual attribute description of the corresponding training image.

Method and apparatus and computer device for automatic semantic annotation for an image

The present application discloses a method and apparatus for obtaining a semantic label of a digital image. An implementation of the method includes: obtaining the digital image; looking up a semantic label model corresponding to the digital image, the semantic label model being used for representing correlation between digital images and semantic labels, and a semantic label being used for literally describing a digital image; and introducing the digital image into the semantic label model to obtain full-image recognition information and local recognition information corresponding to the digital image, and combining the full-image recognition information and the local recognition information to form a semantic label, the full-image recognition information being a summarized description of the digital image, and the local recognition information being a detailed description of the digital image. According to the implementation, the digital image is obtained first, then a semantic label model corresponding to the digital image is looked up, and a semantic label is obtained by using the semantic label model, which may improve the accuracy of obtaining the semantic label corresponding to the digital image.

Method and apparatus for obtaining semantic label of digital image

A contents filter based on similarity of content character and correlation of subject matter includes a filtering system and a disciplining system, and the contents filter isn't a filtering system used for a special subject matter but a general subject matter, the filtering contents can be obtained by leaning of the disciplining system, the filtering system and the disciplining system are installed of physical separation, the filtering system communicates with the disciplining system through the data interface, the filtering system can be installed in the input device of network information. To achieve the different filtering effect the different filtering character obtained by the disciplining system are set to the filtering system located in the different input device of network information. The present invention implements filtering through analyzing and determining text contents, and offers an intelligent and effective service of contents safety for user. The use of the filter is in great agility. Furthermore the filter can identify the contents character to be filtered according to the character with disciplined class provided by user. The processing speed is fast and the filter can be conveniently installed.

Contents filter based on the comparison between similarity of content character and correlation of subject matter

Growing community of inventors

Beijing, China

Jiang Tao Wang