Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

Abstract: A text-video recommendation model determines relevance of a text to a video in a text-video pair (e.g., as a relevance score) with a text embedding and a text-conditioned video embedding. The text-conditioned video embedding is a representation of the video used for evaluating the relevance of the video to the text, where the representation itself is a function of the text it is evaluated for. As such, the input text may be used to weigh or attend to different frames of the video in determining the text-conditioned video embedding. The representation of the video may thus differ for different input texts for comparison. The text-conditioned video embedding may be determined in various ways, such as with a set of the most-similar frames to the input text (the top-k frames) or may be based on an attention function based on query, key, and value projections.

Abstract: This application provides a communication method and a communications device. One example method includes: receiving, by a first communications device, first information from a third communications device; and sending, by the third communications device, the first information to the first communications device.

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Abstract: The present disclosure relates to the field of wireless communications technologies, relates to a signal sending device, a signal receiving device, a symbol timing synchronization method, and a system, and resolves a problem that complexity of symbol timing synchronization performed by a terminal with relatively low crystal oscillator accuracy is high. In a receiving device, a receiving module receives a synchronization signal including a first signal and a second signal. The first signal includes N1 generalized ZC sequences, and the second signal includes N2 generalized ZC sequences. The second signal is used to distinguish different cells or different cell groups. There are at least two generalized ZC sequences with different root indexes in (N1+N2) generalized ZC sequences.

Abstract: This application provides a communication method and a communications device. One example method includes: receiving, by a first communications device, first information from a third communications device; and sending, by the third communications device, the first information to the first communications device.

Abstract: An object detection model and relationship prediction model are jointly trained with parameters that may be updated through a joint backbone. The offset detection model predicts object locations based on keypoint detection, such as a heatmap local peak, enabling disambiguation of objects. The relationship prediction model may predict a relationship between detected objects and be trained with a joint loss with the object detection model. The loss may include terms for object connectedness and model confidence, enabling training to focus first on highly-connected objects and later on lower-confidence items.

Abstract: A communication method and a communications apparatus are provided. The communication method includes: sending, by a terminal device, a first indication message, where the first indication message is used to indicate a first time type and/or a first time precision, or the first indication message is used to indicate an access network device to send time information to the terminal device; receiving, by the terminal device, the time information; and synchronizing, by the terminal device, a time of the terminal device based on the time information. Correspondingly, a communications apparatus is further provided. According to the embodiments of this application, the terminal device can obtain, based on requirements of different application scenarios, a time type and/or time precision preferred by the terminal device.

Abstract: This application provides a communication method and a communications device. The method includes: obtaining, by a first communications device, authorization information, where the authorization information indicates that a second communications device is a device that needs to perform time synchronization; and providing, by the first communications device, time information for the second communications device based on the authorization information; or obtaining, by the first communications device, authorization information, where the authorization information indicates that the second communications device is not a device that needs to perform time synchronization; and skipping, by the first communications device, providing time information for the second communications device based on the authorization information, to avoid broadcasting the time information to all communications devices, so that a time synchronization service can be provided for a specific communications device.

Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

Abstract: A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted classes of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of an expected occurrence of a corresponding one of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval.

Abstract: This application pertains to the field of sensors, and in particular, relates to a millimeter-wave radar, for example, a 77 GHz millimeter-wave radar, and provides an antenna structure, a radar, and a terminal, so that an operating bandwidth of an antenna can be extended. The solution in this application may be used for precision detection and distance detection in the field of autonomous driving or intelligent driving. The antenna structure includes a power division unit, a first antenna bay, and a second antenna bay. The first antenna bay is connected to a first output end of the power division unit, and the second antenna bay is connected to a second output end of the power division unit.

Abstract: This application provides a communication method and a communications apparatus. The communication method includes: determining, by a transmit end device, a first moment, where the first moment is a moment when a receive end device needs to deliver first data to an upper layer of a communications protocol layer of the receive end device; and sending, by the transmit end device, first indication information and the first data, where the first information is used to instruct the receive end device to deliver the first data to the upper layer of the communications protocol layer of the receive end device at the first moment. The receive end device can be supported in determining the first moment based on the first indication information and delivering the first data to the upper layer of the communications protocol layer at the first moment.

Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

Abstract: In the method, the network device sends first signaling to a first terminal device, the first signaling is used to instruct the first terminal device to perform, on an unlicensed spectrum, a listen before talk LBT procedure used for occupying an unlicensed frequency band, and send uplink information within a first time length on the unlicensed frequency band; the network device receives, on the unlicensed spectrum, the uplink information from the first terminal device within the first time length, where the first time length is a part of maximum channel occupancy time obtained by the first terminal device on the unlicensed spectrum, and the first time length is less than the maximum channel occupancy time.

Abstract: A time synchronization method and an apparatus are disclosed. The method includes: receiving, by a terminal device, a first time sent by an access network device; obtaining a first transmission delay; obtaining a fourth time based on the first transmission delay and the first time; and synchronizing a time of the terminal device based on the fourth time.