Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.

Abstract: Embodiments are disclosed for generating temporally consistent manipulated videos. A method of generating temporally consistent manipulated videos comprises receiving a target appearance and an input digital video including a plurality of frames, generating a plurality of target appearance frames from the plurality of frames, training a video prediction network to generate a digital video wherein a subject of the digital video has its appearance modified to match the target appearance, providing the input digital video to the video prediction network, and generating, by the video prediction network, an output digital video wherein the subject of the output digital video has its appearance modified to match the target appearance.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for extracting moments of interest (e.g., video frames, video segments) from a video. In an example embodiment, independent and/or orthogonal machine learning models are used to extract different types of features considering different modalities, and each frame in the video is assigned an importance score for each model. The importance scores for each model are combined into an aggregated importance score for each frame in the video. Depending on the embodiment, the aggregated importance scores are used to visualize the score per frame, identify moments of interest, automatically crop down the video into a highlight reel, browse or visualize the moments of interest within the video, and/or search across multiple videos.

Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

Abstract: Some embodiments provide a sensor data-processing system which detects and classifies objects detected in an environment via fusion of sensor data representations generated by multiple separate sensors. The sensor data-processing system can fuse sensor data representations generated by multiple sensor devices into a fused sensor data representation and can further detect and classify features in the fused sensor data representation. Feature detection can be implemented based at least in part upon utilizing a feature-detection model generated via one or more of deep learning and traditional machine learning. The sensor data-processing system can adjust sensor data processing of representations generated by sensor devices based on external factors including indications of sensor health and environmental conditions.

Abstract: Sensor data captured at by different sensors may be shared across different sensor processing pipelines. Sensor processing pipelines may process captured sensor data from respective sensors. Some of the sensor data that is received or processed at one sensor data processing pipeline may be provided to another sensor data processing pipeline so that subsequent processing stages at the recipient sensor processing pipeline may process the combined sensor data in order to determine a perception decision. Different types of sensor data may be shared, including raw sensor data, processed sensor data, or data derived from sensor data. A control system may perform control actions based on the perception decisions determined by the sensor processing pipelines that share sensor data.

Abstract: Aspects of the present disclosure involve systems and methods for obtaining real-time road friction coefficient estimations. In one embodiment, a regression function is learned using a training data set which correlates input data measurements arriving from onboard system sensors and coefficient estimations arriving from an extension system. In another embodiment, the learned regression function can be retrieved to obtain real-time road friction coefficient estimations while the system is in motion.

Abstract: A system includes a neural network organized into layers corresponding to stages of inferences. The neural network includes a common portion, a first portion, and a second portion. The first portion includes a first set of layers dedicated to performing a first inference task on an input data. The second portion includes a second set of layers dedicated to performing a second inference task on the same input data. The common portion includes a third set of layers, which may include an input layer to the neural network, that are used in the performance of both the first and second inference tasks. The system may receive an input data and perform both inference tasks on the input data in a single pass. During training, a training sample with annotations for both inference tasks may be used to train the neural network in a single pass.

Abstract: In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, identifying shadow edges in the image, computing gradient information for the image and modifying the gradient information relative to the shadow edges for improved performance of computer functionality in an image processing operation.

Abstract: A soft, weighted constraint imposed upon image locations can be used to provide a more accurate segregation of an image into intrinsic material reflectance and illumination components. The constraint is arranged to constrain all color band variations between the image locations into one integral constraining relationship.

Abstract: A soft, weighted constraint imposed upon image locations can be used to provide a more accurate segregation of an image into intrinsic material reflectance and illumination components. The constraint is arranged to constrain all color band variations between the image locations into one integral constraining relationship.

Abstract: A soft, weighted constraint imposed upon image locations can be used to provide a more accurate segregation of an image into intrinsic material reflectance and illumination components. The constraint is arranged to constrain all color band variations between the image locations into one integral constraining relationship.

Abstract: Users are provided with feedback regarding blurriness of an image in real-time. When an image is received, a blur score is automatically generated in addition to a visual that indicates the extent of blurriness across the picture. The blur score is calculated by aggregating an image_blur_score and optionally a motion_blur_score. A user can also be provided with suggestions on improving image sharpness and help in determining if another image needs to be taken.

Abstract: Multiple sets include multiple scale-spaced pyramids representing each of the image and selected characteristics of the image relevant to the identification of the illumination and material aspects of the image.

Abstract: A method for identifying color-based vectors is provided, for an improved analysis of frames of a video, for locally accurate color/material correspondence information is provided. The correspondence information can be used to improve the identification of illumination and material aspects of each image depicted in temporally spaced frames of a video.

Abstract: A method and system comprising image processing techniques that utilize spatio-spectral information relevant to an image, derived from multiple sets of selectively varied representations of the image to accurately and correctly identify illumination and material aspects of the image is provided. Blend pixels are detected to improve the accuracy of the identification of the illumination and material aspects of the image.

Abstract: A soft, weighted constraint imposed upon image locations can be used to provide a more accurate segregation of an image into intrinsic material reflectance and illumination components. The constraint is arranged to constrain all color band variations between the image locations into one integral constraining relationship.

Abstract: A method and system comprising image processing techniques is provided that utilize spatio-spectral information relevant to an image, derived from multiple sets of selectively varied representations of the image to accurately and correctly identify illumination and material aspects of the image. In an exemplary embodiment of the present invention, a scale-spaced pyramid arrangement is provided to preserve the purity of color from scale to scale, to insure accuracy in the identification of illumination and material aspects of the image.

Abstract: A soft, weighted constraint imposed upon image locations temporally spaced in frames of a video, can be used to provide a more accurate segregation of an image into intrinsic material reflectance and illumination components. The constraint is arranged to constrain all color band variations between the image locations into one integral constraining relationship.

Abstract: In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, providing a multi-class classifier trained to identify edges in an image relative to computer actions to be taken in respect to the respective edges, determined as a function of illumination effects in the image and utilizing the multi-class classifier to classify edges in the image, for identification of computer actions to be taken in respect to the edges in the image.