Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.

Abstract: An electronic device holder, having a base having an insert aperture therein, an insert positioned in the insert aperture, and a one-piece flexible member connected to the base for supporting an electronic device disposed in the insert.

Abstract: The present invention provides a carrier for a handportable radiotelephone comprising handstrap means connectable to first and second ends of a radiotelephone and hanging means by which the carrier is releasably hangable, in a handsfree manner, from the user's accoutrements. Thereby, the present invention provides a suitable radiotelephone with dual mode support options. A first hand-carry mode, where the handstrap means provides auxiliary support to the telephone when the user holds the telephone in his hand in the normal way during a call. And, a second handsfree mode, where the hanging means is attached to the user's accoutrements when the phone is in an on-hook condition, for example. The user's accoutrements may include the user's belt, belt loop, bag or the like.