An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.

A contextual local image recognition module of a device retrieves a primary content dataset from a server and then generates and updates a contextual content dataset based on an image captured with the device. The device stores the primary content dataset and the contextual content dataset. The primary content dataset comprises a first set of images and corresponding virtual object models. The contextual content dataset comprises a second set of images and corresponding virtual object models retrieved from the server.

Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method may include training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images; determining one or more dominant image categories associated with the user based on the determined image categories for the obtained set of images; and determining an image editing user interface for the user based on the determined one or more dominant image categories.

Disclosed are systems and methods for dynamically determining categories for images. A computer-implemented method may include training a neural network to receive an input image and determine one or more image categories associated with the input image; obtaining a set of images associated with a user; determining, using the trained neural network, one or more image categories associated with each image included in the obtained set of images; determining one or more dominant image categories associated with the user based on the determined image categories for the obtained set of images; and determining an image editing user interface for the user based on the determined one or more dominant image categories.

A method and an apparatus for retrieving a target are provided. The method may include: obtaining at least one image and a description text of a designated object; extracting image features of the image and text features of the description text by using a pre-trained cross-media feature extraction network; and matching the image features with the text features to determine an image that contains the designated object.

A method of tracking a container closure used to seal liquid in a container is provided, comprising applying a trackable indicia to a container closure, wherein the trackable indicia is unique to the container closure; recording an associated data set comprising a plurality of parameters related to the liquid in the container; correlating the associated data set with the trackable indicia; applying the container closure to the container to seal the liquid within the container; maintaining a database of the trackable indicia corresponding to the associated data set; and scanning the trackable indicia to retrieve the associated data set from the database. The method of the invention also includes utilization of a container closure having inherent natural imperfections and/or patterns on the top and/or sides of the closure and obtaining scanned images and data thereof which are analyzed and utilized for closure and container identification and likewise serve as scannable trackable indicia.

A method of tracking a container closure used to seal liquid in a container is provided, comprising applying a trackable indicia to a container closure, wherein the trackable indicia is unique to the container closure; recording an associated data set comprising a plurality of parameters related to the liquid in the container; correlating the associated data set with the trackable indicia; applying the container closure to the container to seal the liquid within the container; maintaining a database of the trackable indicia corresponding to the associated data set; and scanning the trackable indicia to retrieve the associated data set from the database. The method of the invention also includes utilization of a container closure having inherent natural imperfections and/or patterns on the top and/or sides of the closure and obtaining scanned images and data thereof which are analyzed and utilized for closure and container identification and likewise serve as scannable trackable indicia.

A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

A method for detecting a defect includes: a measurement image including a wafer edge of a wafer to be detected is acquired; an image region to be detected is determined in the measurement image; feature extraction is performed on the image region to be detected to obtain a pixel distribution characteristic of the image region to be detected; and defect detection is performed on the wafer edge based on the pixel distribution characteristic of the image region to be detected.