Techniques for classifying custom object states in visual content

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

• identifying instances of an object shown in a plurality of visual content items, wherein identifying the instances of the object shown in the plurality of visual content items further comprises applying at least one first machine learning model to the plurality of visual content items, wherein the at least one first machine learning model is trained to classify visual content with respect to whether the visual content shows the object, wherein the at least one first machine learning model includes a basic model and an advanced model, wherein the basic model has a domain which is a subset of a domain of the advanced model, wherein applying the at least one first machine learning model to the plurality of visual content items further comprises:
• applying the basic model to the plurality of visual content items;
• selecting a portion of the plurality of visual content items based on outputs of the basic model;
• and
• applying the advanced model to the selected portion of the plurality of visual content items, wherein the plurality of training samples is selected based on outputs of the advanced model;
• labeling a plurality of training samples selected from at least a portion of the plurality of visual content items with respective state labels indicating states of the instances of the object shown in the plurality of visual content items;
• and
• training a second machine learning model using a training set including the plurality of training samples and the respective state labels, wherein the second machine learning model is trained to classify visual content with respect to states of the object shown in the visual content.

A system for training a classifier, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• identify instances of an object shown in a plurality of visual content items, wherein identifying the instances of the object shown in the plurality of visual content items further comprises applying at least one first machine learning model to the plurality of visual content items, wherein the at least one first machine learning model is trained to classify visual content with respect to whether the visual content shows the object, wherein the at least one first machine learning model includes a basic model and an advanced model, wherein the basic model has a domain which is a subset of a domain of the advanced model, wherein the system is further configured to:
• apply the basic model to the plurality of visual content items;
• select a portion of the plurality of visual content items based on outputs of the basic model;
• and
• apply the advanced model to the selected portion of the plurality of visual content items, wherein the plurality of training samples is selected based on outputs of the advanced model;
• label a plurality of training samples selected from at least a portion of the plurality of visual content items with respective state labels indicating states of the instances of the object shown in the plurality of visual content items;
• and
• train a second machine learning model using a training set including the plurality of training samples and the respective state labels, wherein the second machine learning model is trained to classify visual content with respect to states of the object shown in the visual content.