An information processing apparatus includes a storage unit, an image capturing unit, and a processing unit. The storage unit stores a plurality of feature values to be used for object recognition and an update program for the feature values, with respect to each of products registered for sale. The image capturing unit is configured to acquire an image of a product registered for sale. The processing unit is configured to extract a feature value of the product from the acquired image, select one of the plurality of the feature values corresponding to the product as a replacement target, by executing the update program corresponding to the product, and replace the selected feature value with the extracted feature value.

A method and apparatus of open set recognition, and a computer-readable storage medium are disclosed. The method comprises acquiring auxiliary data and training data of known categories for open set recognition, training a neural network alternately using the auxiliary data and the training data, until convergence; extracting a feature of data to be recognized for open set recognition, using the trained neural network; and recognizing a category of data to be recognized, based on the feature of the data to be recognized.

A method and apparatus of open set recognition, and a computer-readable storage medium are disclosed. The method comprises acquiring auxiliary data and training data of known categories for open set recognition, training a neural network alternately using the auxiliary data and the training data, until convergence; extracting a feature of data to be recognized for open set recognition, using the trained neural network; and recognizing a category of data to be recognized, based on the feature of the data to be recognized.

The present disclosure provides a method and apparatus of processing an image, a device and a medium, which relates to a field of artificial intelligence, and in particular to a field of deep learning and image processing. The method includes: determining a background image of the image, wherein the background image describes a background relative to characters in the image; determining a property of characters corresponding to a selected character section of the image; replacing the selected character section with a corresponding section in the background image, so as to obtain an adjusted image; and combining acquired target characters with the adjusted image based on the property.

A method of training a cycle generative networks model and a method of building a character library are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology, and which may be applied to a scene such as image processing and image recognition. A specific implementation scheme includes: inputting a source domain sample character into the cycle generative networks model to obtain a first target domain generated character; calculating a character error loss and a feature loss of the cycle generative networks model by inputting the first target domain generated character and a preset target domain sample character into a character classification model; and adjusting a parameter of the cycle generative networks model according to the character error loss and the feature loss. An electronic device and a storage medium are further provided.

Provided is a training method for a character generation model, and a character generation method, apparatus and device, which relates to the technical field of artificial intelligences, particularly, the technical field of computer vision and deep learning. The specific implementation schemes are: a source domain sample word and a target domain style word are input into the character generation model to obtain a target domain generation word; the target domain generation word and a target domain sample word are input into a pre-trained character classification model to calculate a feature loss of the character generation model; and a parameter of the character generation model is adjusted according to the feature loss.

A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.

The present disclosure relates to a tag-based font recognition system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the tag-based font recognition system jointly trains a font tag recognition neural network with an implicit font classification attention model to generate font tag probability vectors that are enhanced by implicit font classification information. Indeed, the font recognition system weights the hidden layers of the font tag recognition neural network with implicit font information to improve the accuracy and predictability of the font tag recognition neural network, which results in improved retrieval of fonts in response to a font tag query. Accordingly, using the enhanced tag probability vectors, the tag-based font recognition system can accurately identify and recommend one or more fonts in response to a font tag query.

A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into an encoder of the font generation model to obtain a sample character content feature and an association character style feature. The sample character content feature and the association character style feature are input into an attention mechanism network to obtain a target domain style feature. The sample character content feature and the target domain style feature are input into a decoder to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network of the font generation model to obtain a model loss, and a parameter of the font generation model is adjusted according to the model loss.

A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into an encoder of the font generation model to obtain a sample character content feature and an association character style feature. The sample character content feature and the association character style feature are input into an attention mechanism network to obtain a target domain style feature. The sample character content feature and the target domain style feature are input into a decoder to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network of the font generation model to obtain a model loss, and a parameter of the font generation model is adjusted according to the model loss.