Abstract: A system and method for structured querying. A method includes embedding text of a request to create an embedding. The embedding is compared to query structure examples. Each query structure example includes natural language text and a structured query. The structured query of each query structure example is structured according to a format used by a database. Matching query structure examples are identified among the query structure examples based on the comparison. A first query is generated based on the matching query structure examples and the text of the request. The first query is provided to a language model in order to obtain a set of outputs. At least a portion of the database is queried using a second query. The second query is based on the set of outputs from the language model and is structured according to the format used by the database.

Abstract: A system and method for augmented machine learning. A method includes synthesizing a plurality of second visual content samples, wherein synthesizing the plurality of second visual content samples further comprises removing at least a portion of a plurality of first visual content samples with respect to an object in order to create a plurality of removed portion visual content items and providing the plurality of removed portion visual content items to a generative machine learning model, wherein the generative machine learning model is trained to generate at least a portion of visual content with respect to the plurality of removed portion visual content items; creating a training set including the synthesized visual content samples; and training a machine learning model using the training set, wherein the machine learning model is trained to classify visual content with respect to the object.

Abstract: A system and method for training a classifier. A method includes identifying instances of an object shown in visual content items by applying at least one first machine learning model to the visual content items. The at least one first machine learning model is trained to classify visual content with respect to whether the visual content shows the object. Training samples selected from at least a portion of the visual content items are labeled with respective state labels indicating states of the instances of the object shown in the visual content items. A second machine learning model is trained using a training set including the training samples and the respective state labels. The second machine learning model is trained to classify visual content with respect to states of the object shown in the visual content.

Abstract: Systems and methods for visual content processing. A method includes obtaining a subset of media content selected based on outputs of a first machine learning model, wherein the first machine learning model is produced by training a student model using outputs of a teacher model, wherein the outputs of the first machine learning model include a plurality of first predictions for a plurality of portions of the media content; and applying a second machine learning model to the obtained subset of media content, wherein the second machine learning model outputs a plurality of second predictions for respective portions of the plurality of portions, wherein a domain used by the first machine learning model is a subset of a domain used by the second machine learning model.

Abstract: Systems and methods for visual content processing. A method includes applying teacher models to training candidates in order to output instances of a custom object label. The training candidates are selected using a student model based on search configuration parameters. A first set of media content is generated by labeling the training candidates based on the instances of the custom object label output by the teacher models. A custom model is created using the teacher models. The custom model is a machine learning model trained using the first set of media content. A subset of a second set of media content is obtained. The subset of the second set of media content is selected based on outputs of the custom model as applied to the second set of media content. An advanced machine learning model is applied to the obtained subset of the second set of media content.