Abstract: A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

Abstract: A system and method for generating product recommendations for a customer in an e-commerce retail environment is presented. The system includes a data module configured to extract one or more information for one or more products purchased via the e-commerce retail environment, a first attribute extraction module configured to extract one or more product attributes of interest to the customer and a corresponding sentiment, a product identification module configured to identify one or more products similar to a product of interest, a second attribute extraction module configured to extract one or more similar product attributes and a corresponding sentiment, an attribute comparison module configured to compare the one or more product attributes of interest and the corresponding sentiment with the one or more similar product attributes and the corresponding sentiment, and identify a product for recommendation, and a product recommender configured to recommend to the customer the identified product.

Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

Abstract: A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

Abstract: Embodiments described herein provide a method for obtaining information on product inventory and placement in a retail setting. An image including unannotated image data indicative of the retail setting is received. One or more shelves in the retail setting are determined from the unannotated image data, and the image is segmented into one or more sub-images corresponding to the one or more detected shelves. For each sub-image corresponding to a respective detected shelf, a product name is then and a number of appearances of the product name are detected using text recognition on the respective sub-image. Product inventory information and first placement information are derived based at least in part on the detected number of appearances and a shelf level corresponding to the sub-image.

Abstract: Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

Abstract: A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

Abstract: Embodiments described herein provide a method for obtaining information on product inventory and placement in a retail setting. An image including unannotated image data indicative of the retail setting is received. One or more shelves in the retail setting are determined from the unannotated image data, and the image is segmented into one or more sub-images corresponding to the one or more detected shelves. For each sub-image corresponding to a respective detected shelf, a product name is then and a number of appearances of the product name are detected using text recognition on the respective sub-image. Product inventory information and first placement information are derived based at least in part on the detected number of appearances and a shelf level corresponding to the sub-image.

Abstract: A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.