Abstract: Systems, methods, and non-transitory computer-readable media can provide at least one frame of a content item to a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items. Information describing at least a first salient point of interest that appears in the at least one frame can be obtained from the saliency prediction model. The first salient point of interest can be predicted to be of interest to users accessing the content item. A view-based projection can be applied to a region corresponding to the first salient point of interest, wherein the view-based projection enhances a quality in which the region is presented.

Abstract: Systems, methods, and non-transitory computer-readable media can provide at least one frame of a content item to a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items. Information describing at least a first salient point of interest that appears in the at least one frame can be obtained from the saliency prediction model. The first salient point of interest can be predicted to be of interest to users accessing the content item. A view-based projection can be applied to a region corresponding to the first salient point of interest, wherein the view-based projection enhances a quality in which the region is presented.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. For each video frame of the set of video frames, a plurality of interest points are identified based on an interest point detector. For each video frame of the set of video frames, it is determined whether the video frame depicts the same static image as a next video frame in the set of video frames based on the plurality of interest points identified in each video frame.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. Dynamic regions in each video frame of the set of video frames can be filtered out, wherein each dynamic region represents a region in which a threshold level of movement is detected. A determination can be made for each video frame of the set of filtered video frames, whether the video frame comprises synthetic overlaid text based on a machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. A determination can be made that a first set of consecutive video frames of the set of video frames depicts identical content to a second set of consecutive video frames of the set of video frames, wherein the first set of consecutive video frames and the second set of consecutive video frames satisfy a threshold number of consecutive video frames. The video is identified as a looping video based on the determination that the first set of consecutive video frames depicts identical content to the second set of consecutive video frames.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. A determination can be made that a threshold number of video frames of the set of video frames depict two or more reaction icons of a set of reaction icons. The video can be identified as a poll video based on the determining that the threshold number of video frames of the set of video frames depict two or more reaction icons of the set of reaction icons.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that a content item is being presented through a display screen of the computing device. Information describing one or more salient points of interest that appear during presentation of the content item are determined, wherein the salient points of interest are predicted to be of interest to one or more users accessing the content item. The presentation of at least a first salient point of interest is enhanced during presentation of the content item based at least in part on the information.

Abstract: Systems, methods, and non-transitory computer-readable media can determine at least one salient point of interest in a frame of a content item based at least in part on a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items; determine a barrel projection representation for the frame; and apply a view-based projection to the barrel projection representation for the frame, wherein the view-based projection enhances a quality in which a region corresponding to the at least one salient point of interest is presented.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a saliency prediction model for identifying salient points of interest that appear during presentation of content items, provide at least one frame of a content item to the saliency prediction model, and obtain information describing at least a first salient point of interest that appears in the at least one frame from the saliency prediction model, wherein the first salient point of interest is predicted to be of interest to one or more users accessing the content item.

Abstract: Systems, methods, and non-transitory computer-readable media can determine saliency information describing one or more salient points of interest that appear during presentation of a content item, wherein the salient points of interest are predicted to be of interest to one or more users accessing the content item and embed the saliency information describing the salient points of interest into the content item, wherein the saliency information is capable of being processed during presentation of the content item to enhance the presentation of the content item.

Abstract: Systems, methods, and non-transitory computer-readable media can determine saliency information describing one or more salient points of interest that appear during presentation of a content item, wherein the salient points of interest are predicted to be of interest to one or more users accessing the content item and embed the saliency information describing the salient points of interest into the content item, wherein the saliency information is capable of being processed during presentation of the content item to enhance the presentation of the content item.

Abstract: Systems, methods, and non-transitory computer-readable media can present a plurality of content items in a virtual reality content item. Tracking data associated with a plurality of users that access the virtual reality content item can be obtained. An analysis associated with the plurality of content items based on the tracking data can provided, wherein the analysis indicates one or more attributes associated with the plurality of users.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a saliency prediction model for identifying salient points of interest that appear during presentation of content items, provide at least one frame of a content item to the saliency prediction model, and obtain information describing at least a first salient point of interest that appears in the at least one frame from the saliency prediction model, wherein the first salient point of interest is predicted to be of interest to one or more users accessing the content item.

Abstract: Systems, methods, and non-transitory computer-readable media can determine at least one salient point of interest in a frame of a content item based at least in part on a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items; determine a barrel projection representation for the frame; and apply a view-based projection to the barrel projection representation for the frame, wherein the view-based projection enhances a quality in which a region corresponding to the at least one salient point of interest is presented.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that a content item is being presented through a display screen of the computing device. Information describing one or more salient points of interest that appear during presentation of the content item are determined, wherein the salient points of interest are predicted to be of interest to one or more users accessing the content item. The presentation of at least a first salient point of interest is enhanced during presentation of the content item based at least in part on the information.

Abstract: Systems, methods, and non-transitory computer-readable media can provide at least one frame of a content item to a saliency prediction model, the saliency prediction model being trained to identify salient points of interest that appear in content items. Information describing at least a first salient point of interest that appears in the at least one frame can be obtained from the saliency prediction model. The first salient point of interest can be predicted to be of interest to users accessing the content item. A view-based projection can be applied to a region corresponding to the first salient point of interest, wherein the view-based projection enhances a quality in which the region is presented.

Abstract: A social networking system allows users to establish connections with other users to receive content provided to the social networking system by the other user. The social networking system may recommend an additional user to the user in response to the user establishing a connection with a target user. To recommend an additional user, the social networking system determines a relevance of the additional user to the target user relative to the relevance of other candidate users to the target user. The relevance may be based on a measure of similarity between an additional user and the target user, such as a covariance in the overlap of users connected to both the additional user and to the target user.

Abstract: Apparatus and method for improving the performance of a threshold-based detector or classifier, or a generic detector or classifier and increasing the probability of detecting at least one object in an image using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is introduced to the image data such that the performance of the above-referenced detectors or classifiers is improved without altering the detector's or classifier's parameters. Several stochastic resonance (SR) noise-based detection and classification enhancement schemes are presented. The SR noise-enhanced detection and classification schemes can improve any algorithms and systems. To implement these schemes, the only knowledge that is needed is the original input data (no matter 1D, 2D, 3D or others) and the output (detection results) of the existing algorithms and systems.

Abstract: Multiple candidates are optimized in medical device or feature tracking. Possible locations of medical devices or features for each of a plurality of different times are received. The possible locations of devices are modeled using a probability function. An iterative solution to obtain the maximum of the probability function determines the possible locations to be used as the locations of the medical devices or features for each time. Where two or more medical devices or features are provided with a geometric relationship, such as being connected by a detected guide wire, the probability function may account for the geometric relationship, such as a geodesic distance between the possible locations for the two medical devices.

Abstract: Apparatus and method for improving the performance of a threshold-based detector or classifier, or a generic detector or classifier and increasing the probability of detecting at least one object in an image using novel algorithms and stochastic resonance noise is provided, where a suitable dose of noise is introduced to the image data such that the performance of the above-referenced detectors or classifiers is improved without altering the detector's or classifier's parameters. Several stochastic resonance (SR) noise-based detection and classification enhancement schemes are presented. The SR noise-enhanced detection and classification schemes can improve any algorithms and systems. To implement these schemes, the only knowledge that is needed is the original input data (no matter 1D, 2D, 3D or others) and the output (detection results) of the existing algorithms and systems.