An interface module obtains content comprising one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors comprise a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors comprise a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

An interface module obtains content comprising one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors comprise a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors comprise a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

Provided is a method of obtaining state data indicating a state of a user. The method includes: obtaining estimation models for obtaining pieces of state data existing in a plurality of layers from sensor data obtained by a sensor; obtaining at least one piece of sensor data; obtaining state data of a lower layer from the at least one piece of sensor data, based on the estimation models; and obtaining state data of a higher layer from the state data of the lower layer, based on the estimation models.

Disclosed is an electronic apparatus comprising, a memory configured to store instructions; and at least one processor connected to the memory, and configured to detect at least one object of a first-class object or a second-class object included in a target image by the electronic apparatus using an artificial intelligent algorithm to apply the target image to a learned neural network model, and identify and apply an image-quality processing method to be individually applied to at least one detected object, the neural network model is set to detect an object included in an image, as trained based on learning data such as an image, a class to which the image belongs, information about the first-class object included in the image, and information about the second-class object included in the image.

An electronic device and a method for human segmentation in an image are provided. The method includes obtaining the image comprising at least one human, performing face detection to detect the human in the image, generating a first human segmentation from the image by comparing a pre-defined full human body template and the detected human, generating a second human segmentation from the image, by inputting the image to an artificial intelligence trained model and obtaining a composite human segmentation representing the detected human by using both of the first human segmentation and the second human segmentation.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

Systems, methods, and computer program products for associating a media content clip(s) with other media content clip(s) having a different modality by determining first embedding vectors of media content items of a first modality, receiving a media content clip of a second modality, determining a second embedding vector of the media content clip of the second modality, ranking the first embedding vectors based on a distance between the embedding vectors and the second embedding vector, and selecting one or more of the media content items of the first modality based on the ranking, thereby pairing media content clips based on emotion.

An auxiliary filtering device for face recognition is provided. The auxiliary filtering device is used to exclude an ineligible object to be identified according to the relative relationship between object distances and image sizes, the image variation with time and/or the feature difference between images captured by different cameras to prevent the possibility of cracking the face recognition by using a photo or a video.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.