Abstract: Described are systems and methods to determine body types of individuals represented in content items. The determined body types may be associated with the content items to facilitate indexing, filtering, diversifying, etc. of the content items based on the determined body types. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a 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 body types of individuals represented in each content item while eliminating the need for performing image pre-processing prior to determination of the body type(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: 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 extract image segments from an image and include those extracted image segments in a collage. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc., by the creator of the collage or other users.

Abstract: Compounds of Formula (I) along with processes for their preparation that are useful for treating, managing and/or lessening the diseases, disorders, syndromes or conditions associated with the modulation of cannabinoid (CB) receptors. Methods of treating, managing and/or lessening the diseases, disorders, syndromes or conditions associated with the modulation of cannabinoid (CB) receptors of Formula (I).

Abstract: Compounds of Formula (I) along with processes for their preparation that are useful for treating, managing and/or lessening the diseases, disorders, syndromes or conditions associated with the modulation of cannabinoid (CB) receptors. Methods of treating, managing and/or lessening the diseases, disorders, syndromes or conditions associated with the modulation of cannabinoid (CB) receptors of Formula (I).