Abstract: Systems and methods can be implemented to conduct searches based on images used as queries in a variety of applications. In various embodiments, a set of visual words representing a query image are generated from features extracted from the query image and are compared with visual words of index images. A set of candidate images is generated from the index images resulting from matching one or more visual words in the comparison. A multi-level ranking is conducted to sort the candidate images of the set of candidate images, and results of the multi-level ranking are returned to a user device that provided the query image. Additional systems and methods are disclosed.

Abstract: Representative embodiments disclose mechanisms to perform visual intent classification or visual intent detection or both on an image. Visual intent classification utilizes a trained machine learning model that classifies subjects in the image according to a classification taxonomy. The visual intent classification can be used as a pre-triggering mechanism to initiate further action in order to substantially save processing time. Example further actions include user scenarios, query formulation, user experience enhancement, and so forth. Visual intent detection utilizes a trained machine learning model to identify subjects in an image, place a bounding box around the image, and classify the subject according to the taxonomy. The trained machine learning model utilizes multiple feature detectors, multi-layer predictions, multilabel classifiers, and bounding box regression.

Abstract: Non-limiting examples of the present disclosure relate to object detection processing of image content that categorically classifies specific objects within image content. Exemplary object detection processing may be utilized to enhance visual search processing including content retrieval and curation, among other technical advantages. An exemplary object detection model is implemented to categorically classify an object. In doing, so an exemplary object detection model may classify objects based on: analysis of specific objects within image content, positioning of the objects within the image content and intent associated with the image content, among other examples. The object detection model generates exemplary categorical classification(s) for specific data objects, which may be propagated to enhance processing efficiency and accuracy during visual search processing.

Abstract: Systems and methods can be implemented to conduct searches based on images used as queries in a variety of applications. In various embodiments, a set of visual words representing a query image are generated from features extracted from the query image and are compared with visual words of index images. A set of candidate images is generated from the index images resulting from matching one or more visual words in the comparison. A multi-level ranking is conducted to sort the candidate images of the set of candidate images, and results of the multi-level ranking are returned to a user device that provided the query image. Additional systems and methods are disclosed.

Abstract: A technique for hyperparameter tuning can be performed via a hyperparameter tuning tool. In the technique, computer-readable values for each of one or more machine learning hyperparameters can be received. Multiple computer-readable hyperparameter value sets can be defined using different combinations of the values. In response to a request to start, an overall hyperparameter tuning operation can be performed via the tool, with the overall operation including a tuning job for each of the hyperparameter sets. A computer-readable comparison of the results of the parameter tuning operations can be generated for the hyperparameter sets, with the comparison indicating effectiveness of the hyperparameter sets, as compared to each other, in the tuning jobs.