Abstract: Provided are an interaction method and apparatus in a live streaming room, and a device and a storage medium. The method comprises: in response to a trigger operation for a preset duet entry on a live streaming room page, jumping from the live streaming room page to a camera capture page, wherein a host portrait cutout corresponding to the live streaming room page is displayed on the camera capture page and a capture control is provided on the camera capture page acquiring multimedia information captured by a camera, in response to a trigger operation for the capture control on the camera capture page, wherein the multimedia information comprises an image or a video; and synthesizing the multimedia information with the host portrait cutout to obtain a duet multimedia product.

Abstract: Provided are an interaction method and apparatus in a live streaming room, a device, and a storage medium. The method comprises: in response to a trigger operation for a preset drawing entry on a live streaming room page, jumping to a graphic drawing page from the live streaming room page, a drawing trajectory set for a preset object being displayed on the graphical drawing page; when a drawing stroke on the graphic drawing page is received, matching the drawing stroke with the drawing trajectory; and if it is determined that the drawing stroke is successfully matched with the drawing trajectory, displaying prompt information about successful participation in a preset activity, the preset activity and the preset drawing entry having a correspondence.

Abstract: Disclosed are an image processing method, an image processing device, a neutral network and a training method thereof, and a storage medium. The image processing method includes: obtaining an input image; performing a segmentation process on the input image via a first encoding-decoding network, to obtain a first output feature map and the first segmented image; concatenating the first output feature map with at least one selected from the group consisting of the input image and the first segmented image, to obtain an input of the second encoding-decoding network; and performing a segmentation process on the input of the second encoding-decoding network via a second encoding-decoding network, to obtain the second segmented image. And the first encoding-decoding network and the second encoding-decoding network forms a neural network.

Abstract: Provided are a device (4) and a method for online measuring a spectrum for a laser device. The device (4) for online measuring a spectrum for a laser device includes: a first optical path assembly (G1) and a second optical path assembly (G2), and the second optical path assembly (G2) and the first optical path assembly (G1) constitute a measurement optical path. The second optical path assembly (G2) includes: an FP etalon (15) and a grating (18). The homogenized laser beam passes through the FP etalon (15) to generate an interference fringe. The grating (18) is arranged after the FP etalon (15), or is arranged before the FP etalon (15) in the measurement optical path, and is configured to disperse the laser beam passing through the FP etalon (15).

Abstract: Disclosed are an image processing method, an image processing device, a neutral network and a training method thereof, and a storage medium. The image processing method includes: obtaining an input image; performing a segmentation process on the input image via a first encoding-decoding network, to obtain a first output feature map and the first segmented image; concatenating the first output feature map with at least one selected from the group consisting of the input image and the first segmented image, to obtain an input of the second encoding-decoding network; and performing a segmentation process on the input of the second encoding-decoding network via a second encoding-decoding network, to obtain the second segmented image. And the first encoding-decoding network and the second encoding-decoding network forms a neural network.

Abstract: A computer-implemented method is provided. The computer-implemented method includes classifying an image using a classification model having a residual network. Classifying the image using the classification model includes inputting an input image into the residual network having N number of residual blocks sequentially connected, N?2, (N?1) number of pooling layers respectively between two adjacent residual blocks of the N number of residual blocks, and (N?1) number of convolutional layers respectively connected to first to (N?1)-th residual blocks of the N number of residual blocks; processing outputs from the first to the (N?1)-th residual blocks of the N number of residual blocks respectively through the (N?1) number of convolutional layers; vectorizing outputs respectively from the (N?1) number of convolutional layers to generate (N?1) number of vectorized outputs; vectorizing an output from a last residual block of the N number of residual blocks to generate a last vectorized output.

Abstract: A computer-implemented method is provided. The computer-implemented method includes classifying an image using a classification model having a residual network. Classifying the image using the classification model includes inputting an input image into the residual network having N number of residual blocks sequentially connected, N?2, (N?1) number of pooling layers respectively between two adjacent residual blocks of the N number of residual blocks, and (N?1) number of convolutional layers respectively connected to first to (N?1)-th residual blocks of the N number of residual blocks; processing outputs from the first to the (N?1)-th residual blocks of the N number of residual blocks respectively through the (N?1) number of convolutional layers; vectorizing outputs respectively from the (N?1) number of convolutional layers to generate (N?1) number of vectorized outputs; vectorizing an output from a last residual block of the N number of residual blocks to generate a last vectorized output.