Abstract: Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.

Abstract: Embodiments of a contextualized visual search (CVS) system are disclosed capable of isolating target images of items that contain instances of a previously-unseen query image from a large database of target images. In embodiments, the system is used to implement an interactive query interface of an e-commerce portal, which allows the user to specify the query image (e.g. a logo) to be searched. The system converts the query image into a feature vector using a first machine learning model, and compares the feature vector to feature vectors of target images using a second machine learning model to find matching target images that contain an instance of the query image. The system then returns a query result indicating a list of items associated with matched target images. In embodiments, the query results may be ranked based on a set of personalized factors associated with the user.