Abstract: A messaging system may receive a plurality of messages in a conversation maintained by a messaging service. It may be determined that a first message sent from a first messaging application associated with a first user comprises information identifying a second user. The messaging system may receive information indicative of a date associated with the first message. At least one database may store an association among at least the first message, the second user, and the date. The messaging system may send information indicative of the date still being active in the at least one database to a second messaging application associated with the second user.

Abstract: Disclosed herein are methods and systems for performing instance segmentation that can provide improved estimation of object boundaries. Implementations can include a machine-learned segmentation model trained to estimate an initial object boundary based on a truncated signed distance function (TSDF) generated by the model. The model can also generate outputs for optimizing the TSDF over a series of iterations to produce a final TSDF that can be used to determine the segmentation mask.

Abstract: Disclosed herein are methods and systems for performing instance segmentation that can provide improved estimation of object boundaries. Implementations can include a machine-learned segmentation model trained to estimate an initial object boundary based on a truncated signed distance function (TSDF) generated by the model. The model can also generate outputs for optimizing the TSDF over a series of iterations to produce a final TSDF that can be used to determine the segmentation mask.

Abstract: Disclosed herein are methods and systems for performing instance segmentation that can provide improved estimation of object boundaries. Implementations can include a machine-learned segmentation model trained to estimate an initial object boundary based on a truncated signed distance function (TSDF) generated by the model. The model can also generate outputs for optimizing the TSDF over a series of iterations to produce a final TSDF that can be used to determine the segmentation mask.

Abstract: Systems and methods for predicting instance geometry are provided. A method includes obtaining an input image depicting at least one object. The method includes determining an instance mask for the object by inputting the input image into a machine-learned instance segmentation model. The method includes determining an initial polygon with a number of initial vertices outlining the border of the object within the input image. The method includes obtaining a feature embedding for one or more pixels of the input image and determining a vertex embedding including a feature embedding for each pixel corresponding an initial vertex of the initial polygon. The method includes determining a vertex offset for each initial vertex of the initial polygon based on the vertex embedding and applying the vertex offset to the initial polygon to obtain one or more enhanced polygons.

Abstract: Disclosed herein are methods and systems for performing instance segmentation that can provide improved estimation of object boundaries. Implementations can include a machine-learned segmentation model trained to estimate an initial object boundary based on a truncated signed distance function (TSDF) generated by the model. The model can also generate outputs for optimizing the TSDF over a series of iterations to produce a final TSDF that can be used to determine the segmentation mask.

Abstract: The present disclosure is directed to generating high quality map data using obtained sensor data. In particular a computing system comprising one or more computing devices can obtain sensor data associated with a portion of a travel way. The computing system can identify, using a machine-learned model, feature data associated with one or more lane boundaries in the portion of the travel way based on the obtained sensor data. The computing system can generate a graph representing lane boundaries associated with the portion of the travel way by identifying a respective node location for the respective lane boundary based in part on identified feature data associated with lane boundary information, determining, for the respective node location, an estimated direction value and an estimated lane state, and generating, based on the respective node location, the estimated direction value, and the estimated lane state, a predicted next node location.