Systems and methods for logo detection

A computer-readable storage medium storing instructions that, when executed by a computing system having a memory and a processor, cause the computing system to perform a method for logo detection, the method comprising:

• receiving an image corresponding to a logo;
• receiving an indication of a brand associated with the logo;
• generating a set of positive feature vectors at least in part by, for each of a plurality of synthetic advertisements,
• applying an image encoder to the synthetic advertisement to generate a positive feature vector for the synthetic advertisement;
• generating a set of negative feature vectors at least in part by, for each of a plurality of advertisements that do not include the logo,
• applying the image encoder to the advertisement to generate a negative feature vector for the advertisement;
• generating a plurality of classification models at least in part by, selecting a classification model architecture, and
• for each of a plurality of parameters associated with the selected classification model architecture,
• identifying a range of acceptable values associated with the parameter,
• randomly determining a value within the identified range, and
• assigning the randomly determined value to the parameter;
• for each of the generated plurality of classification models,
• training the classification model based at least in part on the set of positive feature vectors and the set of negative feature vectors, and
• scoring the classification model;
• identifying, from among the trained classification models, the classification model with the highest score;
• and
• storing the identified classification model in association with the brand and the logo.

A computing system, comprising at least one processor and at least one memory, for logo detection, the computing system comprising:

• a component configured to receive an image corresponding to a logo;
• a component configured to receive an indication of a brand associated with the logo;
• a component configured to generate a plurality of synthetic advertisements;
• a component configured to generate a set of positive feature vectors at least in part by, for each of the synthetic advertisements, applying an image encoder to the synthetic advertisement to generate a positive feature vector for the synthetic advertisement;
• a component configured to generate a set of negative feature vectors at least in part by, for each of a plurality of advertisements that do not include the logo, applying the image encoder to the advertisement to generate a negative feature vector for the advertisement;
• a component configured to apply the image encoder to each of a plurality of advertisements that do not include the logo to generate a set of negative feature vectors;
• a component configured to select a classification model architecture;
• a component configured to generate a plurality of classification models at least in part by,
• for each of a plurality of parameters associated with the selected classification model architecture,
• identifying a range of acceptable values associated with the parameter,
• randomly determining a value within the identified range, and
• assigning the randomly determined value to the parameter;
• a component configured to, for each of the generated plurality of classification models,
• train the classification model based at least in part on the set of positive feature vectors and the set of negative feature vectors, and
• score the classification model;
• a component configured to identify, from among the trained classification models, the classification model with the highest score;
• and
• a component configured to store the identified classification model in association with the brand and the logo,
• wherein each of the components comprises computer-executable instructions stored in the at least one memory for execution by the computing system.