Jd.com American Technologies Corporation

Beijing Jingdong Shangke Information Technology Co., Ltd.

Beijing Wodong Tianjun Information Technology Co., Ltd.

Nokia Technologies Oy

Hui Zhou

Shanglin Yang

Li Chen

Yi Xu

Shizhu Liu

Timo Juhani Ahonen

Huasong Shan

Yuzhang Wu

Huiman Hou

Jianxun Sun

Min Li

Qingan Yan

Jixing Wang

Kailin Huang

Xin Li

Ning Xu

Johan Windmark

Chi Zhang

Yuhao Zhang

Guanghan Ning

Xiaochuan Fan

Xiangjun Liu

Feng Du

Tobias Karlsson

Dan Miao

Amer Mustajbasic

Qin Wang

Zizhen Wang

Chengchong Zhang

Wanyan Zheng

Fusheng Guo

Yingqiu Tian

Ching-Hui Chen

Karim Benchemsi

Yang Tao

Timo Ahonen

A method and a system for recommending a target garment matching an inputted garment. The method includes: extracting attributes from text description and image of the inputted garment to obtain extracted attributes; querying a knowledge graph using the extracted attributes to obtain matched attributes; retrieving candidate products from a garment pool using the matched attributes; extracting features from the inputted garment and the candidate products; determining the target garment from the candidate products based on grading scores between the features of the inputted garment and the features of the candidate products; and recommending the target garment. The knowledge graph includes nodes corresponding to type of clothes, category of clothes, attribute keys, values of attribute keys, context keys, values of context keys, combination of the values of the attribute keys and the type of clothes, and combination of the value of the attribute keys and the category of clothes.

System and method for product recommendation based on multimodal fashion knowledge graph

A system for knowledge graph construction. The system includes a computing device. The computing device has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: define entities and relations of the knowledge graph; provide documents having sentences; convert the sentences into fix length sentence embeddings and regard the sentence embeddings as primary capsule layers; use a set transformer to learn entity capsules and relation capsules from the primary capsule layers; for each triple, project head and tail entities from entity space to the specific relation space, and determine the relation exists when the sum of the projected head entity vector and the relation vector substantially equals to the projected tail entity vector; and construct the knowledge graph using the head entity, the tail entity, and the determined relation.

System and method for knowledge graph construction using capsule neural network

Systems and method for lifelong tag learning. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code is configured to: provide product descriptions and seed tags characterizing products; train a named-entity recognition (NER) model using the product descriptions and the seed tags; predict pseudo tags from the product descriptions using the NER model; calculate confidence scores of the pseudo tags; compare the confidence scores with a threshold, and define the pseudo tags as true tags when the confidence scores are greater than the threshold; add the true tags to the seed tags to obtain updated tags; and repeat the steps of training, predicting, calculating, comparing and adding using the product descriptions and the updated tags, so as to keep updating the updated tags.

System and method for scalable tag learning in e-commerce via lifelong learning

A system and a method for training an inference model using a computing device. The method includes: providing a text-to-vector converter; providing the inference model and pre-training the inference model using labeled fashion entries; providing non-labeled fashion entries; separating each of the non-labeled fashion entries into a target image and target text; converting the target text into a category vector and an attribute vector using the text-to-vector converter; processing the target image using the inference model to obtain processed target image and target image label; comparing the category vector to the target image label; when the category vector matches the target image label, updating the target image label based on the category vector and the attribute vector to obtain updated label; and retraining the inference model using the processed target image and the updated label.

System and method for fashion attributes extraction

A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

System and method for quality assessment of product description

A system and a method for obtaining a content plan are provided. The system includes a computing device configured to: provide a tag for each clause of each sentence of each product description of each of a category of products, where the tags include introductive tags corresponding to attributions of the products and description tags corresponding to descriptions of the products; determine tag exclusiveness, tag similarity, tag distance, and tag causality between the tags; generate candidate tag sequences the product descriptions; and filter the candidate tag sequences using the tag exclusiveness, the tag similarity, the tag distance and the tag causality to obtain the content plan.

System and method for automatic generation of knowledge-powered content planning

A system and a method for multimedia data analysis. The system includes a computing device. The computer device includes a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: encode task description and task meta info into concatenated task vectors; encode context text and context meta info into concatenated context text vectors; encode context image and the context meta info into concatenated context image vectors; perform dual coattention on the concatenated task vectors and the concatenated context text and image vectors to obtain attended task vectors and attended context vectors; perform BiLSTM on the attended task vectors and the attended context vectors to obtain task encoding and context encoding; and decode the task encoding and the context encoding to obtain an answer to the task.

System and method for semantic analysis of multimedia data using attention-based fusion network

A system for three dimensional (3D) model texturing. The system includes a computing device configured to: instruct a camera to capture images of an object; construct a 3D model of the object based on the images; texture the 3D model using keyframes of the images to obtain a textured 3D model; generate guidance images by projecting the textured 3D model to camera poses of the keyframes; for each guidance image and keyframe pair: modify the guidance image and the keyframe based on their correspondence to obtain enhanced guidance image and warped image, combine the enhanced guidance image and the warped image to obtain harmonized image, and project color from the harmonized images to the 3D model to obtain textured 3D model.

System and method for texture mapping based on guidance prior

A method and system for generating a three-dimensional (3D) model of a garment. The method includes: providing an image of the garment; generating a 3D garment representation based on the image; registering the 3D garment representation to a 3D model to obtain a preliminary 3D garment model; projecting the preliminary 3D garment model dressed on the 3D model to an 2D projected image; and comparing the 2D projected image with the image of the garment, so as to refine the preliminary 3D garment model to obtain the final 3D model of the garment.

System and method for automatically generating three-dimensional virtual garment model using product description

A method for virtual fitting, includes providing a customer avatar and a garment model of a selected garment; registering the garment model to the customer avatar to obtain a dressed garment; capturing a motion image of the customer; performing simulation to the customer avatar and the dressed garment based on the motion image; rendering the simulated customer avatar-garment; and displaying the rendered avatar-garment on a displaying device. Rigging is performed to the dressed garment to obtain rigged garment, and cloth simulation is performed to the dressed garment model to obtain simulated cloth. Each vertex of the simulated cloth has a distance to nearest body mesh vertices on the simulated customer avatar, and the simulated garment is the rigged garment when the distance is less than 0 or the distance is greater than a threshold distance. Air mesh between the avatar and the garment is used for collision detection.

System and method for virtual fitting

A method and system for constructing an evolving ontology database. The method includes: receiving a plurality of data entries; calculating semantic similarity scores between any two of the data entries; clustering the data entries into a multiple current themes based on the semantic similarity scores; selecting, new concepts from the current themes by comparing the current themes with a plurality of previous themes prepared using previous data entries; and updating the evolving ontology database using the new concepts. The semantic score between any two of the data entries are calculated by: semantic similarity score=&#x3a0;se, where sis weight of features sources, fis a feature similarity between the two of the data entries, wis a weight of f, and j, k and n are positive integers.

System and method for building an evolving ontology from user-generated content

A method and system for monitoring an e-commerce platform using artificial intelligence and fixing malfunctions on the e-commerce platform. The method includes: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user using AI processors; and classifying the vector to determine function corresponding to the feedback and status of the function using AI classifiers. The content includes text, voice, image and video; the context includes time, location and submission channel of the feedback; the profile includes attributes, history and preference of the user. Dimensions of the vector respectively corresponding to the text, voice, image, video, time, location, submission channel, attributes, history, and preference of the user.

System and method for monitoring online retail platform using artificial intelligence and fixing malfunction

A method and system for generating an article of a product. The method includes: receiving a request from a user; when the request include an identification of the product, retrieving traits of the product from a trait database, when the request include keywords, retrieving the traits of the product by comparing the similarity between the keywords and the traits or the synonym of the traits; generating candidate sentences corresponding to the traits; selecting sentences from the candidate sentences, and revising and rearranging the sentences to generate the article.

System and method for automatically generating articles of a product

A method for simultaneous localization and mapping. The method includes the step of detecting two-dimensional (2D) feature points from a current frame captured by a camera; matching the 2D feature points from the current frame directly to three-dimensional (3D) map points in a 3D map, so as to obtain correspondence between the 2D feature points and the 3D map points; and computing a current pose of the camera based on the obtained correspondence. Each of the 2D feature points and the 3D map points has a feature descriptor. The step of matching is performed by comparing the feature descriptors of the 2D feature points and the feature descriptors of the 3D map points.

System and method for monocular simultaneous localization and mapping

Embodiments of the present disclosure disclose a method and an apparatus for determining a planar surface. A specific embodiment of the method comprises: determining, from obtained images of the environment that include a to-be-determined planar surface, feature points belonging to the to-be-determined planar surface; judging whether the number of the feature points belonging to the to-be-determined planar surface is smaller than a preset number threshold; if yes, generating first prompt information for prompting a user to add feature points on the to-be-determined planar surface; obtaining at least two first images including the added feature points; and in response to the number of the feature points belonging to the to-be-determined planar surface in the first images being no less than the preset number threshold, reconstructing the to-be-determined planar surface based on the first images so as to determine a location and an orientation of the to-be-determined planar surface. This embodiment implements reconstruction of a weakly-textured or texture-less planar surface.

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Sunnyvale, CA, United States of America

Hui Zhou