Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: A neck fan and a fan assembly are provided. The fan assembly includes: a first air guiding assembly, configured to intake air and to generate an airflow; a second air guiding assembly, fluidly communicated to the first air guiding assembly and disposed at a downstream of the airflow generated by the first air guiding assembly. The second air guiding assembly comprises a plurality of static blades, the plurality of static blades are spaced apart from each other having an equal interval, the plurality of static blades are configured to concentrate the airflow and to guide the airflow to continue flowing.

Abstract: A neck fan includes an arc-shaped shell and a fan assembly. The arc-shaped shell is configured to be worn around a neck of a user. The shell has a first side and a second side opposite to the first side. Each of the first side and the second side defines air inlets. At least one of the first side and the second side defines air outlets. The shell further defines a receiving space, and the receiving space includes an air duct communicating with the air inlets and outlets. The fan assembly is received in the receiving space and configured to drive the air to flow from the air inlets of both sides, through the air duct, reaching the air outlets.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.