Amazon Technologies, Inc.

Alexander Ratnikov

Ahmed Aly Saad Ahmed

Mohamed Kamal Omar

Muhammad Raffay Hamid

Xiaohang Sun

Travis Silvers

Hanxiao Deng

Ivan Ryndin

Tai-Ching Li

Yash Chaturvedi

Jingxiang Chen

Steven James Cox

Prasanth Saraswatula

Systems, devices, and methods are provided for determining contextually relevant user-based product recommendations based on scene information. In at least one embodiment, techniques described herein may be used to determine, using a first machine-learning model, first information associated with a first object within a first image of digital content, determine, using a second machine-learning model, similarity scores between the first object and a first plurality of products of an online purchasing system, detect, in association with the first image of the digital content, performance of a first computer-based action by a user, determine, using a third machine-learning model and based on contextual data of the user, one or more affinity scores for the user, select a first product based on the one or more affinity scores, and present a recommendation to the user to perform a second computer-based action in association with the first product.

Contextually relevant user-based product recommendations based on scene information

Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.

Frame-based body part detection in video clips

Techniques are disclosed for detecting a type of prurient activity shown by a portion of video content. In an example, a machine learning model of a computer system may receive a second portion of video content, the machine learning model including a neural network that is trained to analyze a temporal dimension of the second portion. The machine learning model determines a score indicating a likelihood that the video content shows the type of prurient activity based in part on applying a three-dimensional filter to the second portion of the video content. The computer system then generates a video clip that includes at least the portion of the video content showing the type of prurient activity based on the score, and provides the video clip for display.

Detecting prurient activity in video content

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Redmond, WA, United States of America

Alexander Ratnikov