Hulu, LLC

Yunsheng Jiang

Xiaohui Xie

Brian T Morrison

Yaqi Wang

Kaiwen Deng

Peng Wang

Liangliang Li

Christopher Russell Kehler

Jiarui Yang

Ran Cao

Diman Shen

In some embodiments, a method receives a history of videos that were viewed on a video delivery system as a first sentence in a sequential order and a target video as a second sentence as input to a prediction network. The prediction network analyzes representations for the history of videos and a representation of the target video. The prediction network generates a session representation based on bidirectionally analyzing a sequence of the first representations and the second representation. The method uses the session representation to determine whether to recommend the target video.

Content recommendation generation using content based on bi-directional prediction

In some embodiments, a method selects a sequence of programs watched by a user account. The method calculates a first set of weights based on comparing content of the sequence of programs to content of a target program and calculates a second set of weights based on an order of the sequence of the programs and the first of weights. The first set of weights and the second set of weights are applied to the sequence of programs to generate a prediction of a similarity of the sequence of programs to the target program. Then, the method outputs the prediction of the similarity for use in determining a recommendation for the user account.

Watch sequence modeling for recommendation ranking

In one embodiment, a method receives a set of frames from a video at a first classifier. The first classifier classifies the set of frames with classification scores that indicate a confidence that a frame contains end credit content using the first classifier using a first model that classifies content from the set of frames. A second classifier then refines the classification scores from neighboring frames in the set of frames using a second classifier using a second model that classifies classification scores from the first classifier. A boundary point is selected between a frame in the set of frames considered not including end credit content and a frame in the set of frames including end credit content based on the refined classification scores.

Content type detection in videos using multiple classifiers

In some embodiments, a first detector generates a first output based on a first probability that an image was inserted in a video. The first detector is trained with a set of known images to detect the set of known images. A second detector generates a second output based on a second probability that an image was inserted in the video. The second detector is used to detect the set of unknown images without training. The method analyzes the first output from the first detector based on the probability of the image existing in the video and the second output from the second detector based on the probability of the image existing in the video to generate a combined score from the first output and the second output. An indication of whether the image is detected in the video is output based on the combined score.

Image detection using multiple detection processes

In some embodiments, a method detects a first set of frames in a video that include lines of text, the detecting performed at a frame level on each individual frame. A first representation is generated from the first set of frames and a second representation is generated from the first set of frames. The method filters the first representation based on a number of lines of text within a space in the space dimension to select a second set of frames and filters the second representation based on a number of frames within time intervals in the time dimension to select a third set of frames. Frames in both the second set of frames and the third set of frames are analyzed to determine whether the lines of text in both the second set of frames and the third set of frames are burned-in subtitles.

Frame level and video level text detection in video

In one embodiment, a system detects objects in an image and generates attention regions that are positioned in the image based on first positions of the objects in the image. Focus points for the objects are generated for the attention regions at one or more second positions. Focus boxes are generated using the second positions of the focus points. Then, the system generates information for a motion effect using content of the image based on a number of the focus boxes and third positions of the focus boxes.

Growing community of inventors

Beijing, China

Yunsheng Jiang