Abstract: In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.

Abstract: A system for browsing, searching and/or viewing video content includes at least one user device and a server computer operably connected to the at least one user device. The server computer includes at least one processor operably connected to an electronic storage device, and the at least one processor is programmed with computer program instructions that, when executed, cause the server computer to present a first video on a user interface to the at least one user device, wherein the user interface presents scenes of the first video and semantic labels associated with the scenes of the first video, and wherein the user interface further presents confidence parameters associated with the scenes of the first video and the semantic labels.

Abstract: In certain embodiments, a neural network may be trained to associated context information with multimedia items. In some embodiments, context predictions for multimedia items may be obtained via a neural network. A first multimedia item and a first task related to a first context prediction for the first multimedia item may be presented on a user interface. A user response to the first task may be obtained via the user interface. Based on the user response to the first task, prediction feedback related to the first context prediction or the first multimedia item may be provided to the neural network to cause the neural network to be updated based on the prediction feedback.

Abstract: In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Abstract: Systems and methods for browsing, searching, and/or viewing video with associated semantic labels via a user interface are presented herein. Presentation of a user interface may be effectuated. The user interface may be configured to display scenes of one or more videos and/or one or more semantic labels associated with the scenes. A first scene associated with a first video and a first semantic label associated with the first scene may be displayed in the user interface. Playback of the first video in the user interface may cause one or more changes in the display of semantic labels over time, such that responsive to playback of the first video including display of a second scene after the first scene, a second semantic label is displayed and the first semantic label is no longer displayed. The second semantic label may be associated with the second scene.

Abstract: in certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.

Abstract: Systems and methods for browsing, searching, and/or viewing video with associated semantic labels via a user interface are presented herein. Presentation of a user interface may be effectuated. The user interface may be configured to display scenes of one or more videos and/or one or more semantic labels associated with the scenes. A first scene associated with a first video and a first semantic label associated with the first scene may be displayed in the user interface. Playback of the first video in the user interface may cause one or more changes in the display of semantic labels over time, such that responsive to playback of the first video including display of a second scene after the first scene, a second semantic label is displayed and the first semantic label is no longer displayed. The second semantic label may be associated with the second scene.

Abstract: A system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items. The one or more hardware processors may be further configured to obtain an indication conveying a concept to be learned from the one or more digital media items. The one or more hardware processors may be further configured to receive feedback associated with individual ones of the one or more digital media items. The one or more hardware processors may be configured to obtain individual neural network representations for the individual ones of the one or more digital media items. The one or more hardware processors may be configured to determine a trained concept based on the feedback and the neural network representations of the one or more digital media items.

Abstract: In certain embodiments, a neural network may be trained to associated context information with multimedia items. In some embodiments, context predictions for multimedia items may be obtained via a neural network. A first multimedia item and a first task related to a first context prediction for the first multimedia item may be presented on a user interface. A user response to the first task may be obtained via the user interface. Based on the user response to the first task, prediction feedback related to the first context prediction or the first multimedia item may be provided to the neural network to cause the neural network to be updated based on the prediction feedback.

Abstract: In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.

Abstract: In certain embodiments, automated routing of media items between user devices may be facilitated. In some embodiments, a routing computer system may automatically obtain images or videos from one or more sources. The routing computer system may perform object recognition on the contents of the images or videos to identify individuals or other objects in the images or videos. The routing computer system may assign first and second images or videos of the images or videos to a first media item group based on (i) the first and second images or videos having similar metadata and (ii) the object recognition identifying an individual or object in the first image or video and an individual or object in the second image or video that are similar to each other. The routing computer system may automatically transmit the first image or video to one or more user devices based on the assignment.

Abstract: A system configured for learning new trained concepts used to retrieve content relevant to the concepts learned. The system may comprise one or more hardware processors configured by machine-readable instructions to obtain one or more digital media items. The one or more hardware processors may be further configured to obtain an indication conveying a concept to be learned from the one or more digital media items. The one or more hardware processors may be further configured to receive feedback associated with individual ones of the one or more digital media items. The one or more hardware processors may be configured to obtain individual neural network representations for the individual ones of the one or more digital media items. The one or more hardware processors may be configured to determine a trained concept based on the feedback and the neural network representations of the one or more digital media items.

Abstract: In certain embodiments, media item search and machine learning system training may be facilitated. In some embodiments, a first set of media items may be obtained (based on performance of a query) and presented on a user interface. A user selection of a media item of the first set may be obtained, and the query may be updated based on the user-selected media item. A second set of media items may be obtained based on performance of the updated query, and media items of the second set may be assigned to a group based on their similarities with one another. A predicted name for the group may be determined via a machine learning system and presented on the user interface. A user-indicated update to the predicted name for the group may be obtained and provided to the machine learning system to train the machine learning system.

Abstract: In certain embodiments, automated routing of media items between user devices may be facilitated. In some embodiments, a routing computer system may automatically obtain images or videos from one or more sources. The routing computer system may perform object recognition on the contents of the images or videos to identify individuals or other objects in the images or videos. The routing computer system may assign first and second images or videos of the images or videos to a first media item group based on (i) the first and second images or videos having similar metadata and (ii) the object recognition identifying an individual or object in the first image or video and an individual or object in the second image or video that are similar to each other. The routing computer system may automatically transmit the first image or video to one or more user devices based on the assignment.

Abstract: In certain embodiments, media item search and machine learning system training may be facilitated. In some embodiments, a first set of media items may be obtained (based on performance of a query) and presented on a user interface. A user selection of a media item of the first set may be obtained, and the query may be updated based on the user-selected media item. A second set of media items may be obtained based on performance of the updated query, and media items of the second set may be assigned to a group based on their similarities with one another. A predicted name for the group may be determined via a machine learning system and presented on the user interface. A user-indicated update to the predicted name for the group may be obtained and provided to the machine learning system to train the machine learning system.

Abstract: In certain embodiments, a neural network may be trained to associated context information with multimedia items. In some embodiments, context predictions for multimedia items may be obtained via a neural network. A first multimedia item and a first task related to a first context prediction for the first multimedia item may be presented on a user interface. A user response to the first task may be obtained via the user interface. Based on the user response to the first task, prediction feedback related to the first context prediction or the first multimedia item may be provided to the neural network to cause the neural network to be updated based on the prediction feedback.

Abstract: In certain embodiments, a neural network may be trained to associated context information with multimedia items. In some embodiments, context predictions for multimedia items may be obtained via a neural network. A first multimedia item and a first task related to a first context prediction for the first multimedia item may be presented on a user interface. A user response to the first task may be obtained via the user interface. Based on the user response to the first task, prediction feedback related to the first context prediction or the first multimedia item may be provided to the neural network to cause the neural network to be updated based on the prediction feedback.

Abstract: In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.

Abstract: Systems and methods for browsing, searching, and/or viewing video with associated semantic labels via a user interface are presented herein. Presentation of a user interface may be effectuated. The user interface may be configured to display scenes of one or more videos and/or one or more semantic labels associated with the scenes. A first scene associated with a first video and a first semantic label associated with the first scene may be displayed in the user interface. Playback of the first video in the user interface may cause one or more changes in the display of semantic labels over time, such that responsive to playback of the first video including display of a second scene after the first scene, a second semantic label is displayed and the first semantic label is no longer displayed. The second semantic label may be associated with the second scene.

Abstract: In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.