Abstract: Techniques for using attention events for audience expansion are provided. In one technique, first interaction data that indicates multiple interactions by the first entity with multiple content items is stored. The interactions includes an interaction that is based on an amount of time that content within one of the content items was presented to the first entity. Based on the first interaction data, similarity data that identifies one or more content delivery campaigns that are similar to a particular content delivery campaign is generated. Second interaction data that indicates interaction(s) by a second entity with content item(s) is stored. Based on the second interaction data and the similarity data, association data that associates the second entity with the particular content delivery campaign is stored. The association data may be used to identify the particular campaign in response to receiving a content request from a computing device of the second entity.

Abstract: Technologies for associating an entity with a content delivery campaign are provided. Disclosed techniques include determining a first value of a profile attribute of the entity. A particular node that matches the first value is identified from a value tree of nodes. A parent node of the particular node is identified from the value tree. Child nodes of the parent node are identified, where the child nodes do not include the particular node. Values from the child nodes are then associated with the profile attribute of the entity. A particular value is received for a particular targeting criterion of the content delivery campaign. It is determined whether the particular value matches a value of the child nodes, where the particular value does not match the first value. In response to determining that the particular value matches a value of the child nodes, associating the entity with the content delivery campaign.

Abstract: A fully convolutional pyramid network and method for object (e.g., pedestrian) detection are disclosed. In one embodiment, the object detection system is a pedestrian detection system that comprises: a multi-scale image generator to generate a set of images from an input image, the set of images being versions of the input image at different scales; a human body-specific fully convolutional network (FCN) model operable to generate a set of detection results for each image in the set of images that is indicative of objects that are potentially of human bodies; and a post processor to combine sets of detection results generated by the FCN model for the set of images into an output image with each object location determined as potentially being a human body being marked.

Abstract: A fully convolutional pyramid network and method for object (e.g., pedestrian) detection are disclosed. In one embodiment, the object detection system is a pedestrian detection system that comprises: a multi-scale image generator to generate a set of images from an input image, the set of images being versions of the input image at different scales; a human body-specific fully convolutional network (FCN) model operable to generate a set of detection results for each image in the set of images that is indicative of objects that are potentially of human bodies; and a post processor to combine sets of detection results generated by the FCN model for the set of images into an output image with each object location determined as potentially being a human body being marked.