Yahoo Assets LLC

Yahoo Ad Tech LLC

Eric McVoy Dodds

Paloma De Juan

Kapil Raj Thadani

Benjamin Jackson Culpepper

Simao Herdade

Sanika Shirwadkar

Xingyue Zhou

Yueh-Ning Ku

Praveen Mareedu

Gaurav Srivastava

Gaurav Batra

Sricharanya Venkataramani

Benjamin J Culpepper

Ritesh Kumar Shyam Sunder Agrawal

Huy Xuan Nguyen

Alex J Shaw

Lakshmi V Kesiraju

One or more computing devices, systems, and/or methods for generating a video in a target language are provided. In an example, a first video, in which a first speaker speaks in a first language, is identified. A translated transcript in a second language is determined. The translated transcript is indicative of a translation of speech spoken by the first speaker in the first video. Based upon the translated transcript and a speaker profile associated with a second speaker, first audio, including an auditory representation of the translated transcript being spoken in a voice of the second speaker, is generated. Based upon the first video and the first audio, a second video, in which mouth movements of the first speaker are aligned with speech of the auditory representation of the first audio, is generated.

System and method for generating video in target language

One or more systems and/or methods for product similarity detection and recommendation are provided. Users may view articles and/or other content that includes images depicting products that may be of interest to the users. These images are processed using image processing functionality such as computer vision to identify the products depicted by the images. A vector embedding model is used to generate product vector representations of the products. Catalog items that are available from a catalog to supplement the articles and other content may be processed to generate catalog item vector representations. When content (an article) with an image depicting a product is to be displayed to the user, similarity between a product vector representation of the product and the catalog item vector representations is determined in order to identify and display catalog items depicting products that are similar to the product depicted by the image in the content.

Method, non-transitory machine readable medium, and computing device for product similarity detection and recommendation

One or more systems and/or methods for combining vectors output by multiple different mechanisms for content item retrieval are provided. An image encoder may output a first set of vectors generated by an image model using an input image as input. A text encoder may output a second set of vectors generated by a text model using input text as input. A vector combination module may combine the first set of vectors and the second set of vectors to create a vector output. A weight is applied to the vector output to create a weighted output. An output vector is generated based upon a combination of the first set of vectors, the second set of vectors, and the weighted output. The output vector is used to query a catalog to identify a content item related to the input image and the input text.

Combining vectors output by multiple different mechanisms for content item retrieval

One or more computing devices, systems, and/or methods for a model training framework are provided. A definition and configuration of a model are received. Computations that the model will perform during training of the model are setup based upon the definition and configuration. Summary statistics to be tracked during training are specified. A batch of training data is input into the model to train the model based upon hyper parameters specified in the configuration of the model, and the summary statistics are tacked during the training. The parameters of the model are updated based upon a function corresponding to accuracy of the model processing the training data. The summary statistics are outputted.

Model training framework

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Berkeley, CA, United States of America

Eric McVoy Dodds