Abstract: The embodiments of the application discloses a method and apparatus for pose estimation, and an electronic device. A specific implementation of the method includes: obtaining an observation information sequence corresponding to a human target part, wherein the human target part comprises at least one of the following: a head and a hand; determining an initial human joint point feature based on the observation information sequence; and performing feature interaction based on the initial human joint point feature, and estimating a human pose using an interaction feature, so as to obtain human pose information. This implementation makes the estimated human pose more accurate and realistic.

Abstract: Machine learning models can generate outputs such as salient object detection. However, top-performing large-scale models have high computational cost, making the models hard to deploy on resource-constrained devices and to make predictions in real time. A difference convolution reparameterization technique can be used to fuse the sets of parameters learned during training time into a single set of parameters to be used at inference time or at deployment. The technique can increase performance without adding a lot of parameters. Furthermore, an efficient and effective spatial temporal difference convolution block can be used to leverage temporal information to make predictions. The spatial temporal difference convolution block can also apply the difference convolution reparameterization technique to reduce the need to increase parameters to achieve desired performance.

Abstract: Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.

Abstract: Provided are a depth camera based three-dimensional reconstruction method and apparatus, a device and a storage medium. The method includes: acquiring at least two frames of images obtained by capturing a target scenario by a depth camera; determining, according to the at least two frames of images, relative camera poses in response to capturing the target scenario by the depth camera; by adopting a manner of at least two levels of nested screening, determining at least one feature voxel from each frame of image, where each level of screening adopts a respective voxel partitioning rule; fusing and calculating the at least one feature voxel of each frame of image according to a respective relative camera pose of each frame of image to obtain a grid voxel model of the target scenario; and generating an isosurface of the grid voxel model to obtain a three-dimensional reconstruction model of the target scenario.

Abstract: Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.

Abstract: Methods and systems for ticket classification and response include clustering tickets according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined that includes an invariant portion and a variable portion. A new ticket sub-class, based on the variable portion of the template, is determined that represents a specific sub-type of an existing class. A ticket taxonomy is updated to include the new ticket sub-class. The tickets are labeled according to the updated ticket taxonomy. The tickets are automatically responded to.