Abstract: Local models learned from anomaly detection are used to rank detected anomalies. The local model patterns are defined from image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by fitting extracted image features to the local model patterns. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

Abstract: Systems, methods, and non-transitory computer readable media configured to receive an advertisement including an image. A fraud assessment value for the advertisement can be determined. An image assessment value for the image can be determined. The fraud assessment value and a threshold value for fraud assessment can be compared. The image assessment value and a threshold value for image assessment can be compared. Fraud associated with the advertisement can be determined based on comparison of the fraud assessment value and the threshold value for fraud assessment and comparison of the image assessment value and the threshold value for image assessment.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.

Abstract: Systems, methods, and non-transitory computer-readable media can calculate raw scores for a plurality of media items based on a classifier model and a target concept. The plurality of media items are ranked based on the raw scores. A review set of the plurality of media items is determined, the review set comprising a subset of the plurality of media items. Each of the media items of the review set is associated with a content depiction determination. A normalized score formula is calculated based on the raw scores and the content depiction determinations for the media items of the review set.

Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.

Abstract: Systems, methods, and non-transitory computer-readable media can calculate raw scores for a plurality of media items based on a classifier model and a target concept. The plurality of media items are ranked based on the raw scores. A review set of the plurality of media items is determined, the review set comprising a subset of the plurality of media items. Each of the media items of the review set is associated with a content depiction determination. A normalized score formula is calculated based on the raw scores and the content depiction determinations for the media items of the review set.

Abstract: Systems, methods, and non-transitory computer-readable media can obtain a set of video frames at a first resolution. Process the set of video frames using a convolutional neural network to output one or more signals, the convolutional neural network including (i) a set of two-dimensional convolutional layers and (ii) a set of three-dimensional convolutional layers, wherein the processing causes the set of video frames to be reduced to a second resolution. Process the one or more signals using a set of three-dimensional de-convolutional layers of the convolutional neural network. Obtain one or more outputs corresponding to the set of video frames from the convolutional neural network.

Abstract: Systems, methods, and non-transitory computer-readable media can detect an operation that causes a challenge response process to be initiated. An image category associated with a recognized category label can be identified. At least one image associated with the image category can be displayed during the challenge response process. The operation can be executed when the challenge response process, based on the at least one image, is successfully completed.

Abstract: Systems, methods, and non-transitory computer-readable media can detect an operation that causes a challenge response process to be initiated. An image category associated with a recognized category label can be identified. At least one image associated with the image category can be displayed during the challenge response process. The operation can be executed when the challenge response process, based on the at least one image, is successfully completed.

Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a first image including a representation of a first user. A second image including a representation of a second user can be received. A first set of poselets associated with the first user can be detected in the first image. A second set of poselets associated with the second user can be detected in the second image. The first image including the first set of poselets can be inputted into a first instance of a neural network to generate a first multi-dimensional vector. The second image including the second set of poselets can be inputted into a second instance of the neural network to generate a second multi-dimensional vector. A first distance metric between the first multi-dimensional vector and the second multi-dimensional vector can be determined.

Abstract: Systems, methods, and non-transitory computer-readable media can obtain a first video frame and a second video frame. The first video frame can be processed using a convolutional neural network to output a first set of feature maps. The second video frame can be processed using the convolutional neural network to output a second set of feature maps. The first set of feature maps and the second set of feature maps can be processed using a spatial matching layer of the convolutional neural network to determine an optical flow for at least one pixel.

Abstract: Systems, methods, and non-transitory computer-readable media can calculate raw scores for a plurality of media items based on a classifier model and a target concept. The plurality of media items are ranked based on the raw scores. A review set of the plurality of media items is determined, the review set comprising a subset of the plurality of media items. Each of the media items of the review set is associated with a content depiction determination. A normalized score formula is calculated based on the raw scores and the content depiction determinations for the media items of the review set.

Abstract: Systems, methods, and non-transitory computer-readable media can obtain a set of video frames at a first resolution. Process the set of video frames using a convolutional neural network to output one or more signals, the convolutional neural network including (i) a set of two-dimensional convolutional layers and (ii) a set of three-dimensional convolutional layers, wherein the processing causes the set of video frames to be reduced to a second resolution. Process the one or more signals using a set of three-dimensional de-convolutional layers of the convolutional neural network. Obtain one or more outputs corresponding to the set of video frames from the convolutional neural network.

Abstract: Systems, methods, and non-transitory computer-readable media can determine at least one operation that causes a challenge-response test to be activated for authenticating a user. A first set of content items that each have a threshold similarity to a query content item can be determined. A second set of content items that each have a threshold dissimilarity to the query content item can be determined. The challenge-response test can be provided for display to the user. The challenge-response test presents a group of content items including the first set of content items and the second set of content items.

Abstract: Systems, methods, and non-transitory computer-readable media can obtain a content item to be evaluated by a set of cascaded convolutional neural networks, the set of cascaded convolutional neural networks including at least a first convolutional neural network (CNN) and a second CNN. The content item can be provided to the first CNN as input, wherein an output of the first CNN includes data describing at least one region of interest in the content item and at least one first concept corresponding to the region of interest. The output of the first CNN can be provided to the second CNN as input, wherein an output of the second CNN includes data describing at least one second concept corresponding to the region of interest, the second concept being more accurate than the first concept.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a first image including a representation of a first user. A second image including a representation of a second user can be received. A first set of poselets associated with the first user can be detected in the first image. A second set of poselets associated with the second user can be detected in the second image. The first image including the first set of poselets can be inputted into a first instance of a neural network to generate a first multi-dimensional vector. The second image including the second set of poselets can be inputted into a second instance of the neural network to generate a second multi-dimensional vector. A first distance metric between the first multi-dimensional vector and the second multi-dimensional vector can be determined.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a first image including a representation of a first user. A second image including a representation of a second user can be received. A first set of poselets associated with the first user can be detected in the first image. A second set of poselets associated with the second user can be detected in the second image. The first image including the first set of poselets can be inputted into a first instance of a neural network to generate a first multi-dimensional vector. The second image including the second set of poselets can be inputted into a second instance of the neural network to generate a second multi-dimensional vector. A first distance metric between the first multi-dimensional vector and the second multi-dimensional vector can be determined.

Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.

Abstract: Systems, methods, and non-transitory computer-readable media can identify a set of regions corresponding to a geographical area. A collection of training images can be acquired. Each training image in the collection can be associated with one or more respective recognized objects and with a respective region in the set of regions. Histogram metrics for a plurality of object categories within each region in the set of regions can be determined based at least in part on the collection of training images. A neural network can be developed based at least in part on the histogram metrics for the plurality of object categories within each region in the set of regions and on the collection of training images.