Abstract: Provided is a display panel. The display panel includes: a first substrate and a second substrate disposed opposite to each other, and a liquid crystal layer disposed between the first substrate and the second substrate; wherein the first substrate comprises a first base substrate, a plurality of first signal lines and a plurality of second signal lines disposed on a side, close to the second substrate, of the first substrate; and the second substrate comprises a second base substrate, and a shielding layer disposed on a side, close to the first substrate, of the second base substrate.

Abstract: Embodiments herein provide a vehicle-mounted gas turbine generator set, including: a chassis vehicle, a main cabin body and at least one of an air intake filter system, a ventilation filter system and an exhaust system. The chassis vehicle includes a main beam and an operator cabin. The operator cabin is located at a first side of the main beam in a first direction. The main cabin body defines a main accommodation space. A gas turbine and a generator connected with each other, and an electrical system connected with the gas turbine and the generator, are arranged in the main accommodation space. In a second direction perpendicular to the first direction, the main accommodation space is located between the main beam and the at least of the air intake filter system, the ventilation filter system and the exhaust system.

Abstract: Embodiments of the present disclosure provides a heat dissipating device for an electrical cabinet and the electrical cabinet. The electrical cabinet comprises a cabinet body and a plurality of drawers. The plurality of drawers are inserted into the cabinet body from a front side of the cabinet body in a thickness direction of the cabinet body and arranged along a height direction of the cabinet body. The heat dissipating device comprises a first air inlet and a second air inlet. The first air inlet is arranged on a front side of the cabinet body between two adjacent drawers of the plurality of drawers to receive a first cooling airflow. The second air inlet is arranged on a front side of the cabinet body adjacent to a bottom of the cabinet body to receive a second cooling airflow. The heat dissipating device further comprise an isolation assembly arranged in the cabinet body to isolate the first and second cooling airflows.

Abstract: Embodiments of the present disclosure provides a heat dissipation equipment cabinet. The heat dissipation equipment cabinet comprises: a cabinet comprising a front wall, a rear wall and a pair of sidewalls perpendicular to the front wall, at least one of the front and rear walls comprising at least one cabinet air inlet arranged at bottom and at least one cabinet air outlet arranged at top; and a plurality of drawer assemblies spaced apart from at least one of the pair of sidewalls to form a cabinet heat dissipation channel, a drawer heat dissipation channel communicating with the cabinet heat dissipation channel being formed between every two adjacent drawer assemblies of the plurality of drawer assemblies, and wherein each of the plurality of drawer assemblies has a drawer air inlet and a drawer air outlet. This can improve the heat dissipation effect of the equipment cabinet.

Abstract: Systems and techniques are disclosed for generating a three-dimensional (3D) model. For example, a process can include estimating a plurality of features associated with at least a portion of images; inverse warping the plurality of features into reference pose features having a reference pose; generating filtered reference pose features by selecting features from the reference pose features based on a distance of the selected features from corresponding features from the reference pose; generating modified reference pose features by modifying the filtered reference pose features based on a feature grid associated with a reference model associated with the reference pose; projecting the filtered reference pose features into one or more two dimensional (2D) planes; identifying first features associated with the person from the one or more 2D planes; and generating a 3D model of the person having a pose using the first features, the modified reference pose features, and pose information.

Abstract: A display panel includes a liquid crystal cell, a first polarizing assembly and a second polarizing assembly. The liquid crystal cell includes a first substrate and a second substrate disposed opposite to each other, and a liquid crystal layer disposed between the first substrate and the second substrate. The first polarizing assembly is disposed on a side of the first substrate further away from the liquid crystal layer and includes at least a first polarizing layer having a first transmission axis, and the first polarizing assembly is configured for generating polarized light whose polarization direction is parallel to the first transmission axis. The second polarizing assembly is located on a side of the second substrate further away from the liquid crystal layer and includes at least a transflective layer and a light absorbing layer.

Abstract: The present disclosure provides a display panel and a display device. The display panel includes: a substrate, a common electrode line, a gate line, a first insulating layer, a data line, a second insulating layer, a pixel electrode layer, a first orientation layer, a liquid crystal layer, a second orientation layer, and a common electrode layer; wherein an angle between an extending direction of the common electrode line and a rubbing direction of the first orientation layer ranges from 0 degrees to 30 degrees or from 60 degrees to 90 degrees, and an angle between an extending direction of the gate line and the rubbing direction of the first orientation layer ranges from 0 degrees to 30 degrees or from 60 degrees to 90 degrees.

Abstract: Systems and techniques are provided for performing scene segmentation and object tracking. For example, a method for processing one or more frames. The method may include determining first one or more features from a first frame. The first frame includes a target object. The method may include obtaining a first mask associated with the first frame. The first mask includes an indication of the target object. The method may further include generating, based on the first mask and the first one or more features, a representation of a foreground and a background of the first frame. The method may include determining second one or more features from a second frame and determining, based on the representation of the foreground and the background of the first frame and the second one or more features, a location of the target object in the second frame.

Abstract: A display substrate and a display device relate to the technical field of displaying. The display substrate includes a plurality of display units spaced apart from each other and a plurality of connection units, the plurality of connection units being connected between two adjacent display units; the plurality of connection units include: two connection units arranged along a first direction, wherein the first direction is parallel to a stretching direction of the display substrate; wherein the two connection units arranged along the first direction are axisymmetric with respect to a first reference line, so that the plurality of connection units arranged along a second direction have a same deformation amount in a stretching state, the second direction is perpendicular to the stretching direction, and an extension direction of the first reference line is parallel to the second direction.

Abstract: Systems and techniques are described for image processing. An imaging system receives an identity image and an attribute image. The identity image depicts a first person having an identity. The attribute image depicts a second person having an attribute, such as a facial feature, an accessory worn by the second person, and/or an expression. The imaging system uses trained machine learning model(s) to generate a combined image based on the identity image and the attribute image. The combined image depicts a virtual person having both the identity of the first person and the attribute of the second person. The imaging system outputs the combined image, for instance by displaying the combined image or sending the combined image to a receiving device. In some examples, the imaging system updates the trained machine learning model(s) based on the combined image.

Abstract: Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Abstract: Systems and techniques are described herein for generating an image. For instance, a method for generating an image is provided. The method may include obtaining a source image of a face having source attributes and exhibiting a source pose and source gaze; obtaining at least one of a target pose and a target gaze; and generating a modified image of the face having the source attributes and exhibiting at least one of the target pose and the target gaze.

Abstract: Systems and techniques are described herein for generating one or more three-dimensional models. For instance, a method for generating one or more three-dimensional models is provided. The method may include obtaining a plurality of images of an object; obtaining a plurality of segmentation masks associated with the plurality of images, each segmentation mask of the plurality of segmentation masks including at least one label indicative of at least one segment of the object in a respective image of the plurality of images; training, using the plurality of images and the plurality of segmentation masks, a machine-learning model to generate one or more semantically-labeled three-dimensional models of the object; and generating using the trained machine-learning model, a semantically-labeled three-dimensional model of the object, the semantically-labeled three-dimensional model of the object including at least one label indicative of the at least one segment of the object.

Abstract: Systems and techniques are described herein for determining depth information. For instance, a method for determining depth information is provided. The method may include transmitting electromagnetic (EM) radiation toward a plurality of points in an environment; comparing a phase of the transmitted EM radiation with a phase of received EM radiation to determine a respective time-of-flight estimate of the EM radiation between transmission and reception for each point of the plurality of points in the environment; determining first depth information based on the respective time-of-flight estimates determined for each point of the plurality of points in the environment; obtaining second depth information based on an image of the environment; comparing the first depth information with the second depth information to determine an inconsistency between the first depth information and the second depth information; and adjusting a depth of the first depth information based on the inconsistency.

Abstract: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Abstract: Techniques and systems are provided for image processing. For instance, a process can include obtaining, from one or more image sensors, a first image of an environment; determining semantic labels for a plurality of pixels of the first image based on whether each pixel of the plurality of pixels is associated with an object in the first image to generate a semantically segmented image; applying a two-dimensional line representation of the first image to the semantically segmented image to generate a labeled 2D line representation; back-projecting the labeled 2D line representation to three dimensions to generate a line representation model; fusing the line representation model and the semantically segmented image to generate a labeled line representation model; and outputting the labeled line representation model.

Abstract: Systems and techniques are provided for performing an accurate object count using monocular three-dimensional (3D) perception. In some examples, a computing device can generate a reference depth map based on a reference frame depicting a volume of interest. The computing device can generate a current depth map based on a current frame depicting the volume of interest and one or more objects. The computing device can compare the current depth map to the reference depth map to determine a respective change in depth for each of the one or more objects. The computing device can further compare the respective change in depth for each object to a threshold. The computing device can determine whether each object is located within the volume of interest based on comparing the respective change in depth for each object to the threshold.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include generating a three-dimensional (3D) model of a first portion of an object based on one or more frames depicting the object. The process can also include generating a mask for the one or more frames, the mask including an indication of one or more regions of the object. The process can further include generating a 3D base model based on the 3D model of the first portion of the object and the mask, the 3D base model representing the first portion of the object and a second portion of the object. The process can include generating, based on the mask and the 3D base model, a 3D model of the second portion of the object.

Abstract: Systems and techniques are described for establishing one or more virtual sessions between users. For instance, a first device can transmit, to a second device, a call establishment request for a virtual representation call for a virtual session and can receive, from the second device, a call acceptance indicating acceptance of the call establishment request. The first device can transmit, to the second device, first mesh information for a first virtual representation of a first user of the first device and first mesh animation parameters for the first virtual representation. The first device can receive, from the second device, second mesh information for a second virtual representation of a second user of the second device and second mesh animation parameters for the second virtual representation. The first device can generate, based on the second mesh information and the second mesh animation parameters, the second virtual representation of the second user.

Abstract: System and techniques are described herein for processing images to detect expressions of a subject. In one illustrative example, a method of recognizing facial expressions in one or more images includes obtaining, by a computing device, a first image of a person; obtaining expression information based on the first image and an anchor image associated with the person; and determining an expression classification associated with the first image based on the expression information.