Kwai Inc.

Lg Electronics Inc.

Robert Boschgmbh

Jiayi Liu

Mohak Shah

Huayan Wang

Jayanta Kumar Dutta

Unmesh Kurup

Xin Miao

Samarth Tripathi

A computer-implemented method includes: initializing model parameters for training a neural network; performing a forward pass and backpropagation for a first minibatch of training data; determining a new weight value for each of a plurality of nodes of the neural network using a gradient descent of the first minibatch; for each determined new weight value, determining whether to update a running mean corresponding to a weight of a particular node; based on a determination to update the running mean, calculating a new mean weight value for the particular node using the determined new weight value; updating the weight parameters for all nodes based on the calculated new mean weight values corresponding to each node; assigning the running mean as the weight for the particular node when training on the first minibatch is completed; and reinitializing running means for all nodes at a start of training a second minibatch.

Training a neural network using periodic sampling over model weights

Methods and apparatuses are provided for movie and television series video data analysis. The method includes: gathering and reading, by a processor, a plurality of input movies; removing a video border of each input movie; splitting the input movie into short clips, based on accuracy and efficiency requirements of different analyzing models; assessing attributes of each input movie by analyzing, with the different analyzing models, the input movie, the short clips cut from the input movie, and the frame images extracted from the input movie; and summarizing the plurality of input movies based on matching and integrating the attributes assessed for each input movie.

Methods and device for video data analysis

Methods and apparatuses are provided for editing and generating a short video based upon a long video. The method includes: obtaining a plurality of short source video clips as candidate video clips; obtaining attributes of each short source video clip; obtaining a plurality of target base videos according to a target genre, processing the plurality of target base videos by splitting each target base video into a plurality of short target base video clips, and obtaining attributes of each short target base video clip; selecting short target video clips from the plurality of short source video clips, based on distribution of the attributes obtained for the plurality of the short source video clips and the plurality of short target base video clips; and editing and combining the short target video clips selected from the plurality of short source video clips, to obtain a target video.

Methods and devices for editing and generating videos

A search framework for finding effective architectural building blocks for deep convolutional neural networks is disclosed. The search framework described herein utilizes a building block which incorporates branch and skip connections. At least some operations of the architecture of the building block are undefined and treated as hyperparameters which can be automatically selected and optimized for a particular task. The search framework uses random search over the reduced search space to generate a building block and repeats the building block multiple times to create a deep convolutional neural network.

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Fremont, CA, United States of America

Jiayi Liu