Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating difference captions indicating detected differences in digital image pairs. The disclosed system generates a first feature map of a first digital image and a second feature map of a second digital image. The disclosed system converts, utilizing a linear projection neural network, the first feature map to a first modified feature map in a feature space corresponding to a large language machine-learning model. The disclosed system also converts, utilizing the linear projection neural network layer, the second feature map to a second modified feature map in the feature space corresponding to the large language machine-learning model.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that leverages a series of versions of a digital image to generate a caption prediction. Furthermore, the disclosed systems receive an image difference captioning request that includes a series of versions of a digital image with a series of manipulations applied to the series of versions. Moreover, the disclosed systems access one or more edit descriptions for one or more of the series of manipulations. Further, the disclosed systems generate text inputs from the series of versions of the digital image and the one or more edit description. From the text inputs and using a large language model, the disclosed systems generate a caption prediction that indicates a difference between a first version and a last version of the series of versions of the digital image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to identify regions of an image that have been editorially modified. For example, the image comparison system includes a deep image comparator model that compares a pair of images and localizes regions that have been editorially manipulated relative to an original or trusted image. More specifically, the deep image comparator model generates and surfaces visual indications of the location of such editorial changes on the modified image. The deep image comparator model is robust and ignores discrepancies due to benign image transformations that commonly occur during electronic image distribution. The image comparison system optionally includes an image retrieval model utilizes a visual search embedding that is robust to minor manipulations or benign modifications of images. The image retrieval model utilizes a visual search embedding for an image to robustly identify near duplicate images.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating difference captions indicating detected differences in digital image pairs. The disclosed system generates a first feature map of a first digital image and a second feature map of a second digital image. The disclosed system converts, utilizing a linear projection neural network, the first feature map to a first modified feature map in a feature space corresponding to a large language machine-learning model. The disclosed system also converts, utilizing the linear projection neural network layer, the second feature map to a second modified feature map in the feature space corresponding to the large language machine-learning model.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to identify regions of an image that have been editorially modified. For example, the image comparison system includes a deep image comparator model that compares a pair of images and localizes regions that have been editorially manipulated relative to an original or trusted image. More specifically, the deep image comparator model generates and surfaces visual indications of the location of such editorial changes on the modified image. The deep image comparator model is robust and ignores discrepancies due to benign image transformations that commonly occur during electronic image distribution. The image comparison system optionally includes an image retrieval model utilizes a visual search embedding that is robust to minor manipulations or benign modifications of images. The image retrieval model utilizes a visual search embedding for an image to robustly identify near duplicate images.

Abstract: A modular structural assembly to form part of an aircraft wing that has an outer skin and a plurality of stringers is disclosed. The assembly includes a plurality of panels, each panel having a portion that forms part of the outer skin and at least one stringer integrally formed with said portion.

Abstract: A modular structural assembly to form part of an aircraft wing that has an outer skin and a plurality of stringers is disclosed. The assembly includes a plurality of panels, each panel having a portion that forms part of the outer skin and at least one stringer integrally formed with said portion.

Abstract: A system and method for moderating child interaction in a networked community by a supervisor that includes monitoring interactions of a child within a first environment; selecting a child interaction that satisfies a notification parameter of the supervisor; and sending a notification to the supervisor within a second environment with information associated with the selected interaction.

Abstract: Disclosed are systems and methods that enable an end user to control the parameters, behavior, and identity of network communications regardless of the limitations placed upon these controls by the end user's communications device or communications service provider. An intermediate media proxy resides on a telecommunications network and accepts inbound and outbound communications to and from a network communication device. The intermediate media proxy functions as an intermediary for network communications that occur between the network communications device and other network communications devices. In this manner, the intermediate media proxy can control various features of the communications independent of limitations enacted by the end user's communications device or communications service provider.