Hong Kong Applied Science and Technology Research Institute Company, Limited

Yi Ping Tse

Yang Liu

Zhi Bin Lei

Chao Feng

Yu Keung Ng

Zheng Long Li

Chun Ming Chan

Zuyao Wang

Sung Ho Cheung

Zhixi Li

A visual content search and retrieval platform comprising an object detection model, a coarse-grained image classification model, and a selection of fine-grained image classification models. During inference, the object detection model locates and extracts a query object in a query image; the coarse-grained image classification model classifies the query object's meta-category; the fine-grained image classification model trained and optimized for the meta-category of the query object is selected; and the selected fine-grained image classification model searches and retrieves from an image database images of objects most similar to the query object. The fine-grained image classification model is implemented by a multi-focus fine-grained image search and retrieval apparatus comprising a class learning module learned to generate the query object's overall appearance query features; a local description module learned to generate the query object's local details query features; an outline description module learned to generate the query object's outline query features.

Fine-grained visual content search platform

A method for summarizing text with sentence extraction including steps as follows. Sentences are extracted from a document including text by a natural language processing (NLP) based feature extractor. A word vector set with respect to each of the sentences is generated by a processor. The word vector set with respect to each of the sentences is used to generate a n-grams vector set and a phrase-n vector set with respect to each of the sentences. A word score representing similarity between the word vector sets, a n-grams score representing similarity between the n-grams vector sets, and a phrase-n score representing similarity between the phrase-n vector sets are computed. The word, n-grams, and phrase-n scores are combined to compute an edge score. Text features are selected from the sentences using the edge scores of the sentences, so as to output a summary of the document.

Method of summarizing text with sentence extraction

A method for recognizing and extracting named entities in a natural language input text processing comprising: performing, by a compressed named entities recognition (NER)-model-based named entity recognizer, a first stage NER on the input text to generate a first stage determination of whether at least one named entity exists in the input text; if the first stage NER determines no named entity exists in the input text, performing, by a rule based named entity recognizer, a second stage NER on the input text to generate a second stage NER result; if the first stage NER determines at least one named entity exists in the input text, generating, by the compressed NER-model-based named entity recognizer, a first stage NER result; and integrating, by a NER result integrator, the first stage NER result and the second stage NER result to generate a final NER result.

Efficient and accurate named entity recognition method and apparatus

Growing community of inventors

Hong Kong, China

Yi Ping Tse