Beijing Bytedance Network Technology Co., Ltd.

Lemon Inc.

Bytedance Inc.

Xiaojie Jin

Xiaohui Shen

Yan Wang

Gen Li

Linjie Yang

Xuchen Song

Peng Wang

Xiaochen Lian

Taojiannan Yang

Mingyu Ding

Yi-Wen Chen

Provided are a transition type determination method, an electronic device and a storage medium. The method includes: acquiring a picture matching degree between a candidate transition type and a transition position of two adjacent video clips, and acquiring a music matching degree of the candidate transition type and background music of a video to which the two adjacent video clips belong; and determining a target transition type for the transition position according to the picture matching degree and the music matching degree, where the target transition type is used for a transition effect between the two adjacent video clips.

Transition type determination method and apparatus, and electronic device and storage medium

Systems and methods for obtaining attention features are described. Some examples may include: receiving, at a projector of a transformer, a plurality of tokens associated with image features of a first dimensional space; generating, at the projector of the transformer, projected features by concatenating the plurality of tokens with a positional map, the projected features having a second dimensional space that is less than the first dimensional space; receiving, at an encoder of the transformer, the projected features and generating encoded representations of the projected features using self-attention; decoding, at a decoder of the transformer, the encoded representations and obtaining a decoded output; and projecting the decoded output to the first dimensional space and adding the image features of the first dimensional space to obtain attention features associated with the image features.

Lightweight transformer for high resolution images

Systems and methods for performing neural architecture search are provided. In one aspect, the system includes a processor configured to select a plurality of candidate neural networks within a search space, evaluate a performance of each of the plurality of candidate neural networks by: training each candidate neural network on a training dataset to perform the predetermined task and determining a ranking metric for each candidate neural network based on an objective function. The ranking metric includes a weight-related metric that is determined based on weights of a prediction layer of each respective candidate neural network before and after the respective candidate neural network is trained. The processor is configured to rank the plurality of candidate neural networks based on the determined ranking metrics.

Neural architecture search system using training based on a weight-related metric

A video generation method includes: acquiring an original image corresponding to a target frame, and identifying a target object in the original image; according to a sliding zooming strategy, performing sliding zooming processing on an initial background image in the original image excluding the target object, so as to obtain a target background image, wherein the sliding zooming strategy is at least used for indicating a sliding direction and a zooming direction of the initial background image, and the sliding direction is opposite to the zooming direction; according to the position of the target object in the original image, superimposing an image of the target object onto the target background image to obtain a target image corresponding to the target frame; and generating a target video on the basis of the target image corresponding to the target frame.

Video generation method and apparatus, and readable medium and electronic device

The present disclosure describes techniques of detecting objects in a video. The techniques comprises extracting features from each frame of the video; generating a first attentive feature by applying a first attention model on at least some of features extracted from any particular frame among the plurality of frames, wherein the first attention model identifies correlations between a plurality of locations in the particular frame by computing relationships between any two locations among the plurality of locations; generating a second attentive feature by applying a second attention model on at least one pair of features at different levels selected from the features extracted from the particular frame, wherein the second attention model identifies a correlation between at least one pair of locations corresponding to the at least one pair of features; and generating a representation of an object included in the particular frame.

Detecting objects in a video using attention models

Provided are a transition type determination method, an electronic device and a storage medium. The method includes: acquiring a picture matching degree of a candidate transition type with a transition position between two adjacent video clips, and acquiring a music matching degree of the candidate transition type; determining, based on the acquired image matching degree and the acquired music matching degree, a matching degree of the candidate transition type at the transition position; and determining, according to the matching degree, whether to determine the candidate transition type as a target transition type, where the target transition type is used for a transition effect between the two adjacent video clip.

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Los Angeles, CA, United States of America

Xiaojie Jin