Salesforce, Inc.

Keld Lundgaard

Cameron Wolfe

Amir Hossein Raffiee

Zhichao Wang

Systems and methods are provided for detecting, at a face detection model of a server, a face of a person having a first identity in an image. A face recognition model of the server may detect an identity vector for the detected face of the person in the image. A new identity vector may be generated based on at least the detected identity vector. A revised image may be generated using the generated new identity vector and the image to be output for display, where the face of the person having the first identity in the image has a second identity in the revised image based on the generated new identity vector. The server may transmit the revised image.

Systems and methods of identity removal in images

Methods, computer readable media, and devices for exceeding the limits of visual-linguistic multi-task learning are disclosed. One method may include identifying a multi-modal multi-task classification dataset including a plurality of data examples, creating a transformer machine learning model to predict a plurality of categorical attributes of a product, and training the transformer machine learning model based on the multi-modal multi-task classification dataset using an alpha decay schedule and dynamically allocating task-specific parameters for at least one of the plurality of task-specific classification heads based on task complexity.

Exceeding the limits of visual-linguistic multi-task learning

Systems and methods are provided for generating textual embeddings by tokenizing text data and generating vectors to be provided to a transformer system, where the textual embeddings are vector representations of semantic meanings of text that is part of the text data. The vectors may be averaged for every token of the generated textual embeddings and concatenating average output activations of two layers of the transformer system. Image embeddings may be generated with a convolutional neural network (CNN) from image data, wherein the image embeddings are vector representations of the images that are part of the image data. The textual embeddings and image embeddings may be combined to form combined embeddings to be provided to the transformer system.

Systems and methods of data augmentation for pre-trained embeddings

Systems and methods are provided for determining pairwise scores for items in an electronic catalog that is stored in a storage device communicatively coupled to a server, and determining when a pair of items in the electronic catalog is valid. A sum of the pairwise scores by activity type for a plurality of activity types may be determined, and the pairwise scores for the plurality of activity types may be summed. A sorted list of items that compliment an anchor item of the electronic catalog may be generated. Items from the generated sorted list may be filtered, and an item ranking in the filtered list may adjusted. Consistency between recommendations of the items in the adjusted ranking list may be determined, final recommendations of the items based on the determined consistency between recommendations may be selected and transmitted for display.

Systems and methods of item set generation in an electronic catalog

Systems and methods are provided for receiving, at a server, a selection of an anchor product from an electronic catalog stored in at least one storage device communicatively coupled to the server, and vectorizing at least one of text and images associated with the selected anchor product and other products in the catalog. At least one of key words may be determined from text data and key images from image data for each product of the catalog. Vectors may be formed from at least one of the keywords and key images, and concatenating the separate vectors together to form final vectors for the products. A similarity search may be performed using the final vectors to determine a group of similar products from the vectorized products of the catalog. Selected products that are within a same slot as the anchor product may be labelled in batch.

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Cambridge, MA, United States of America

Keld Lundgaard