Abstract: Disclosed herein are techniques for detecting logos in images or video. In one embodiment, one or more candidate regions are detected for determining logos in an image. A logo is determined to be the logo in the candidate region based on matching a feature vector of a candidate region to a feature vector of the logo.

Abstract: Disclosed herein are techniques for detecting logos in images or video. In one embodiment, a first logo detection model detects, from an image, candidate regions for determining logos in the image. A feature vector is then extracted from each candidate region and is compared with reference feature vectors stored in a database. The logo corresponding to the best matching reference feature vector is determined to be the logo in the candidate region if the best matching meets a certain criterion. In some embodiments, a second logo detection model trained using synthetic training images is used in combination with the first logo detection model to detect logos in a same image.

Abstract: Disclosed herein are techniques for determining brand safety of a video including image frames and audio content. In some embodiments, frame-level features, scene-level features, and video-level features are extracted by a set of frame-level models, a set of scene-level models, and a set of video-level models, respectively. Outputs from lower level models are used as inputs for higher level models. A brand safety score indicating whether it is safe to associate a brand with the video is determined based on the outputs from the set of video-level models. In some embodiments, commercial content associated with the brand is insert into the video that is determined to be safe for the brand.

Abstract: Techniques disclosed herein relate generally to augmenting images or videos with graphics. More specifically, some embodiments relate to realistically or photorealistically augmenting a target image or video frame with a source graph, such as a computer-generated graph or a real world image. In one embodiment, a planar segment of the target image is identified based on a surface normal map of the target image. The planar segment is then used to determine a focal length and a homography function for transforming the source graph.

Abstract: Disclosed herein are techniques for detecting logos in images or video. In one embodiment, a first logo detection model detects, from an image, candidate regions for determining logos in the image. A feature vector is then extracted from each candidate region and is compared with reference feature vectors stored in a database. The logo corresponding to the best matching reference feature vector is determined to be the logo in the candidate region if the best matching meets a certain criterion. In some embodiments, a second logo detection model trained using synthetic training images is used in combination with the first logo detection model to detect logos in a same image.

Abstract: Disclosed herein are techniques for determining brand safety of a video including image frames and audio content. In some embodiments, frame-level features, scene-level features, and video-level features are extracted by a set of frame-level models, a set of scene-level models, and a set of video-level models, respectively. Outputs from lower level models are used as inputs for higher level models. A brand safety score indicating whether it is safe to associate a brand with the video is determined based on the outputs from the set of video-level models. In some embodiments, commercial content associated with the brand is insert into the video that is determined to be safe for the brand.

Abstract: Disclosed herein are techniques for digital content integration. A computer-implemented method includes receiving a target digital content item that includes a plurality of frames, identifying a set of candidate host frames for inserting source digital content items from the plurality of frames based on one or more attributes of the target digital content item, determining a candidate score for each respective candidate host frame of the candidate host frames, and generating host time defining data including identifications and the candidate scores of the candidate host frames, where the candidate score indicates a degree of transition of the target digital content item at the candidate host frame. One or more candidate host frames are then selected based on the candidate scores for inserting one or more source digital content items into the target digital content item.

Abstract: Techniques disclosed herein relate generally to augmenting images or videos with graphics. More specifically, some embodiments relate to realistically or photorealistically augmenting a target image or video frame with a source graph, such as a computer-generated graph or a real world image. In one embodiment, a planar segment of the target image is identified based on a surface normal map of the target image. The planar segment is then used to determine a focal length and a homography function for transforming the source graph.

Abstract: Disclosed herein are techniques for digital content integration. A computer-implemented method includes receiving a target digital content item that includes a plurality of frames, identifying a set of candidate host frames for inserting source digital content items from the plurality of frames based on one or more attributes of the target digital content item, determining a candidate score for each respective candidate host frame of the candidate host frames, and generating host time defining data including identifications and the candidate scores of the candidate host frames, where the candidate score indicates a degree of transition of the target digital content item at the candidate host frame. One or more candidate host frames are then selected based on the candidate scores for inserting one or more source digital content items into the target digital content item.