Lemon Inc.

Beijing Zitiao Network Technology Co., Ltd.

Fan Chen

Longyin Wen

Sijie Zhu

Xin Gu

Kin Chung Wong

Yufei Wang

Lu Xu

The present disclosure describes techniques for searching editing components based on text using a machine learning model. A plurality of visual embeddings indicative of a plurality of visual editing components is acquired by the machine learning model. The plurality of visual embeddings indicative of the plurality of visual editing components is projected into a common space by a first sub-model of the machine learning model. A text query input is received by a user. A text embedding indicative of the text query is generated. The text embedding is projected into the common space by a second sub-model of the machine learning model. At least one visual editing component among the plurality of visual editing components is determined based on the projected text embedding and the plurality of projected visual embeddings in the common space. Information indicative of the at least one visual editing component is displayed via a user interface.

Searching editing components based on text using a machine learning model

The present disclosure describes techniques for generating representations of editing components using a machine learning model. Images and guidance tokens are input into a first sub-model of the machine learning model. The machine learning model is trained to distinguish the editing components from raw materials and generate the representations of the editing components. Tokens corresponding to the images are generated by the first sub-model based on the images and the guidance tokens. The tokens corresponding to the images and the guidance tokens are input into a second sub-model of the machine learning model. The second sub-model comprises a cross-attention mechanism. An embedding indicative of at least one editing component is generated based on the tokens corresponding to the images and the guidance tokens by the second sub-model.

Generating representations of editing components using a machine learning model

Examples are provided relating to media content editing architectures utilizing machine learning techniques. One aspect includes a method for media content editing, the method comprising: receiving a media content from a user; receiving an editing request for the media content from the user; and editing the media content based on the editing request to generate edited media content by: retrieving a prompt from a prompt pool, wherein the retrieved prompt is selected based on the editing request; parsing the retrieved prompt and the editing request using a large language model to generate one or more editing actions to be performed on the media content; and performing the one or more editing actions on the media content to generate the edited media content.

Technical architectures for media content editing using machine learning

Growing community of inventors

Los Angeles, CA, United States of America

Fan Chen