Abstract: Devices, systems, and methods are provided for generating and selecting video clips for inclusion in video sequences based on still frame images. A method may include encoding first embeddings for a first video including first images of an item at a first scene, the first embeddings indicative of features of the first scene; encoding second embeddings for a second video including second images of the item at a second scene, the second embeddings indicative of features of the second scene; encoding third embeddings for the first video, the third embeddings indicative of features of a first type of camera shot used for the first images; encoding fourth embeddings for the second video, the fourth embeddings indicative of features of a second type of camera shot used for the second images; and generating, based on the first, second, third, and fourth embeddings, a video sequence for the item.

Abstract: Technologies are provided for automated generation of directed content campaigns. The generated campaign can be optimized for performance. In some embodiments, a group of variations of attributes that define a directed content campaign can be generated and allocated traffic weights for respective impressions of the directed content campaign in a media outlet channel. The traffic weights can then be iteratively updated until a termination criterion is satisfied. At each iteration, the traffic weight can be updated by applying a machine-learning model to current performance metric values of respective impressions corresponding to the traffic weights. After termination of the updates to the traffic weights, a particular set of variations having traffic weights exceeding a threshold can be selected as directed content campaign having satisfactory performance. Those variations can be supplied to a requestor device for subsequent utilization.